Paper deep dive

Trust in Edge-Enabled IoT Security: Features, Challenges and Research Directions

Esin Ece Aydın, Şerif Bahtiyar, Gürkan Gür

Year: 2026Venue: arXiv preprintArea: cs.CRType: PreprintEmbeddings: 162

Intelligence

Status: not_run | Model: - | Prompt: - | Confidence: 0%

Entities (0)

No extracted entities yet.

Relation Signals (0)

No relation signals yet.

Cypher Suggestions (0)

No Cypher suggestions yet.

Abstract

Abstract:Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things (IoT). To support time-sensitive and resource-constrained applications, IoT systems nowadays increasingly rely on edge computing. This brings computation and decision-making closer to end devices. In edge-enabled IoT architecture, latency and communication overhead are reduced, but interactions among a larger and more diverse set of devices, edge nodes, services, and data sources are introduced as well. In such environments, security and privacy mechanisms provide the foundation for protection, while trust management can assess the reliability of interacting entities and adapting secure decisions. In this paper, we systematically review the current state of trust management in edge-enabled IoT. To this end, we propose a comprehensive taxonomy that maps physical, network, and application architectural IoT layers against the consumer, commercial, industrial, and infrastructure IoT domains. We further investigate state-of-art research based on their trust design, how trust integrated into secure IoT operations, the attacks that effect trust management process. Based on these findings, we identify key gaps in current research and outline future directions for context-aware and adaptive trust management in edge-enabled IoT.

PDF

Open source PDF →Open local PDF →

Trouble viewing inline? Open PDF directly →

Full Text

158,671 characters extracted from source content.

Expand or collapse full text

Trust in Edge-Enabled IoT Security: Features, Challenges and Research Directions CCS: General and reference Surveys and overviewsCCS: Security and privacy Trust frameworksCCS: Security and privacy Security requirements ESİN ECE AYDIN, Affiliation: Cyber Security and Privacy Research Lab., SPFLab, Department of Computer Engineering, Istanbul Technical University, Istanbul, Türkiye email: aydinesi16@itu.edu.tr , ŞERİF BAHTİYAR, Affiliation: Cyber Security and Privacy Research Lab., SPFLab, Department of Computer Engineering, Istanbul Technical University, Istanbul, Türkiye email: bahtiyars@itu.edu.tr and GÜRKAN GÜR, Affiliation: Zurich University of Applied Sciences (ZHAW), Institute of Computer Science (InIT), Winterthur, Switzerland email: gurkan.gur@zhaw.ch Abstract. Providing autonomous intelligence, pervasive connectivity and usability to human life and industry has led to the emergence of the Internet of Things (IoT). To support time-sensitive and resource-constrained applications, IoT systems nowadays increasingly rely on edge computing. This brings computation and decision-making closer to end devices. In edge-enabled IoT architecture, latency and communication overhead are reduced, but interactions among a larger and more diverse set of devices, edge nodes, services, and data sources are introduced as well. In such environments, security and privacy mechanisms provide the foundation for protection, while trust management can assess the reliability of interacting entities and adapting secure decisions. In this paper, we systematically review the current state of trust management in edge-enabled IoT. To this end, we propose a comprehensive taxonomy that maps physical, network, and application architectural IoT layers against the consumer, commercial, industrial, and infrastructure IoT domains. We further investigate state-of-art research based on their trust design, how trust integrated into secure IoT operations, the attacks that effect trust management process. Based on these findings, we identify key gaps in current research and outline future directions for context-aware and adaptive trust management in edge-enabled IoT. Keywords: security, trust management, Internet of Things (IoT), IoT attacks, review 1. Introduction Security and privacy have long been central pillars of research in networked systems. However, securing IoT operations introduces unique challenges that security and privacy alone do not fully address, including reliable data exchange, dependable service provision, and context-aware decision-making (Chandrasekaran and Rajasekaran, 2024; Hossain et al., 2024). Modern IoT ecosystems no longer consist of just Internet-connected devices. Instead, IoT entities form dynamic relationships with neighboring devices, edge servers, cloud services, and autonomous applications. These heterogeneous entities continuously exchange information and rely on one another to complete distributed tasks and provide seamless services. Furthermore, next-generation IoT ecosystems are fundamentally characterized by the integration of edge AI. In this decentralized deployment, intelligence is distributed across edge and end devices rather than being centralized in the cloud. By this change, privacy and security enforcement are also distributed across heterogeneous edge and end nodes (Bounaira et al., 2025; Feng et al., 2023). However, they do not by themselves determine with whom an entity should interact, whose data or services should be relied upon, or how much autonomy should be granted in day-to-day IoT operation. Therefore, trust emerges as an equally critical third dimension. According to (Alhandi et al., 2023), trust enables IoT systems to assess the reliability of relationships between things and realize the full potential of their interactions. From consumer-related ones to infrastructure-wide IoT applications, integrating trust mechanisms is an important aspect for the successful operation of IoT. Besides, as stated in (Verma and Chandra, 2023), trust assessment can make a substantial impact on the overall security of IoT systems. In edge-enabled IoT, trust management are not centralized: models, training data, and score updates can be evaluated by end nodes themselves. This enables privacy-preserving decisions, reduced latency and bandwidth usage. However, local decision making and cross-layer collaboration become important design issues in trust management of next-gen IoT as mentioned by (Ranathunga et al., 2021). Although trust is recognized as a one of the fundamental requirements for reliable IoT operation (Najib et al., 2019; Chandrasekaran and Rajasekaran, 2024), currently, trust remains is an abstract concept without a widely accepted definition. The literature offers a wide spectrum of definitions: ranging from subjective confidence and probabilistic behavioral belief (Mohanta et al., 2025; Li et al., 2024b; Wang et al., 2020; Aaqib et al., 2024), to reputation-based and QoS-driven trustworthiness (Guo et al., 2022b; Verma and Chandra, 2023), and to multi-dimensional, context-aware, or AI-predicted trust (Chandrasekaran and Rajasekaran, 2024; Bampatsikos et al., 2025; Messina et al., 2025a; Ranathunga et al., 2021; Sagar et al., 2024). The literature lacks a consistent view of whether trust concerns devices, data, services, or all three, resulting in fragmented methodologies and inconsistent evaluation. This survey addresses this gap by examining trust in conjunction with security and privacy. It examines the definition of trust in the literature, the processes of trust management, and its integration into IoT operations to improve resilience, robustness, and secure decision-making. This survey is structured around the following research questions: • RQ1 – How is trust management in edge-enabled IoT characterized across application domains, architectural layers, and trust targets (device, data, and service), and what recurring design patterns emerge in trust management? • RQ2 – How do reviewed schemes use trust scores to drive IoT operational decisions, and trust-related attacks, including routing, reputation, data-integrity, and identity-related threats are targeted? • RQ3– What research gaps remain in IoT trust management, and what future research directions are needed for scalable and resource-efficient trust mechanisms, adversary-aware and attack-adaptive models in emerging IoT environments including edge-enabled, agent-based, immersive, and physically–virtually coupled deployments? 1.1. Existing Surveys We compare eighteen recent survey papers published between 2019 and 2026 with our work, as given in Table 1. The comparison covers several aspects of trust management. Trust target indicates trust subjects each survey focuses. Trust design taxonomy indicates whether the survey provides a structured trust-management design, including evidence acquisition, trust modeling, dissemination, and maintenance. Operational roles of trust capture how trust is used in practice to support IoT decisions, such as access control, secure routing, intrusion detection, service selection, or data aggregation. Trust attack analysis indicates whether trust-related attacks are systematically identified and analyzed. The last one, architecture scope, gives the IoT deployment targeted by each survey. Table 1. Comparison of Existing Surveys on Trust Management in IoT with Our Work Survey Year Trust Targets Trust Design Operational Trust Attack Architecture Device Data Service Taxonomy Roles of Trust Analysis Scope Xu et al. (Xu et al., 2026) 2026 ✓ ✓ ∼ ✓ ∼ ✓ Edge-Enabled IoV Wang et al. (Wang et al., 2026) 2026 ✓ ∼ ∼ ✓ ✓ Distributed IoV D’Aniello & Fotia (D’aniello and Fotia, 2025) 2025 ✓ ∼ ∼ Edge-Enabled IoT Jmal et al. (Jmal et al., 2025) 2025 ✓ ✓ ∼ ✓ SIoT Kamble et al. (Kamble et al., 2024) 2024 ✓ ✓ ∼ General IoT Sagar et al. (Sagar et al., 2024) 2024 ✓ ✓ ✓ ✓ ✓ SIoT AlMarshoud et al. (AlMarshoud et al., 2024) 2024 ✓ ∼ ✓ ∼ Decentralized IoV Tyagi et al. (Tyagi et al., 2023) 2023 ✓ ✓ ✓ ✓ WSN-Based IoT Shirvani & Masdari (Shirvani and Masdari, 2023) 2023 ✓ ✓ ∼ ✓ ∼ General IoT Aaqib et al. (Aaqib et al., 2023) 2023 ✓ ∼ ✓ General IoT Lenard et al. (Lenard et al., 2023) 2023 ✓ ∼ ✓ ∼ ✓ Distributed IoT Alhandi et al. (Alhandi et al., 2023) 2023 ✓ ✓ ✓ ∼ WSN-Based IoT Wang et al. (Wang et al., 2022a) 2022 ✓ ∼ ∼ ✓ ✓ ✓ Heterogeneous IoT Konsta et al. (Konsta et al., 2022) 2022 ✓ ∼ ✓ ∼ Edge-Enabled IoT Muzammal et al. (Muzammal et al., 2020) 2021 ✓ ✓ ∼ ✓ ✓ General IoT Marche & Nitti (Marche and Nitti, 2020) 2020 ✓ ✓ ∼ ∼ ✓ SIoT Pourghebleh et al. (Pourghebleh et al., 2019) 2019 ✓ ✓ ∼ ∼ ∼ General IoT Najib et al. (Najib et al., 2019) 2019 ✓ ✓ General IoT Our work 2026 ✓ ✓ ✓ ✓ ✓ ✓ Edge-Enabled IoT ✓= explicitly covered, ∼ = partially covered, blank = not covered Across all the surveys examined, device trust is the only target treated consistently. This reflects that IoT trust management has device-centric model, where IoT nodes, sensors are evaluated for their trustworthiness. While a few recent surveys have begun to expand their scope such as (Shirvani and Masdari, 2023; Alhandi et al., 2023; Muzammal et al., 2020; Pourghebleh et al., 2019) including data trust, and (Jmal et al., 2025; Sagar et al., 2024; Marche and Nitti, 2020) focusing on service trust which is unsurprising given their focus on Social IoT and service recommendation or selection. To the best of our knowledge, no prior survey paper covers all three trust targets; the closest cases are the review by (Xu et al., 2026; Wang et al., 2026; Wang et al., 2022a; Muzammal et al., 2020), however, they still address one or two of the remaining trust targets only partially. According to (Sagar et al., 2024), trust management schemes are categorized into recommendation-based, reputation-based, prediction-based, and policy-based groups. However, this classification combines two different aspects into a single taxonomy: recommendation and reputation-based schemes describe the source of trust evidence, whereas prediction and policy-based schemes describe how trust is computed. Since these dimensions are independent, a single scheme may belong to multiple categories simultaneously. Therefore, in this survey, trust evidence sources and trust computation mechanisms are treated as separate dimensions. (Aaqib et al., 2023; Najib et al., 2019) classify trust models across a broad architectural scope, ranging from centralized to distributed IoT systems. However, these surveys do not analyze the operational roles of trust or trust-related attacks. (Pourghebleh et al., 2019; Muzammal et al., 2020) provide more detailed discussions of trust computation methods and adversarial threats. (Kamble et al., 2024) frames trust management as a core enabler of secure IoT applications across five main domains: agriculture, education, healthcare, home security, and transportation. The authors further organize prior work using a five-part taxonomy, including trust measures (QoS, social-based trust), trust circulation (centralized, distributed), trust algorithms (fuzzy logic, machine learning, Bayesian methods, entropy-based models, regression), trust generation (single, multi attribute), and highlighting trust-related attacks. However, the study lacks an evaluation of the operational roles of trust within IoT systems. While (Shirvani and Masdari, 2023) categorizes the operational roles of trust, it does not offer any taxonomy for trust management design, and it omits giving any structure how trust is addressed in state-of-the-art approaches. Edge-enabled IoT architectures are examined in (Xu et al., 2026; D’aniello and Fotia, 2025; Konsta et al., 2022) where trust is maintained distributed across devices, fog/edge nodes, and cloud rather than confined to a centralized structures. They emphasize decentralized mechanisms such as blockchain, and edge-local reputation. (Konsta et al., 2022) reviews state-of-the-art approaches together with the threat models. The authors categorize 53 papers and evaluate each one on seven dimensions: information gathering, trust formation, propagation and update, threat model, experiments, and simulator. Different taxonomies are suggested based on layers, deployment modes, algorithms, or enabling technologies. However, the taxonomies in (D’aniello and Fotia, 2025; Jmal et al., 2025; Shirvani and Masdari, 2023; Marche and Nitti, 2020; Pourghebleh et al., 2019) remain incomplete with respect to the full trust lifecycle. 1.2. Contributions & Paper Organization Building on the the research questions outlined in Section 1 and comparison in Table 1, this survey makes the following contributions: • We present a systematic literature review of trust-management techniques in edge-enabled IoT and classify the selected studies. We use a taxonomy that maps each study across IoT layers and four application domains: consumer, commercial, industrial, and infrastructure IoT. • We provide an analysis of trust-related attacks by relating adversarial objectives, attacker behavior patterns, and affected stages of the trust-management process. We further examine how trust-management outputs are used to inform decisions and trigger actions within IoT systems. • We identify key research challenges in building trustworthy edge-enabled next-generation IoT. Then, we outline possible future research directions. The remainder of this paper is organized as follows. First, Section 2 provides background, gives definitions, and explains trust management process along the trust components. Next, Section 3 presents the systematic review methodology we follow for comparative analysis. Then, representative state-of-the-art frameworks are analyzed and discussed in Section 4. Section 5 discusses open research challenges, including architectural, trust-model, adversarial, and ecosystem barriers, and outlines future directions. Finally, Section 6 concludes the paper. 2. Trust, Security and Privacy Foundations in IoT Security in IoT concerns protecting devices, communication channels, and services against unauthorized access, manipulation, and disruption with confidentiality, integrity, and availability as its core objectives. However, IoT deployments are heterogeneous, resource-constrained, and large-scale which makes conventional security mechanisms difficult to apply (Deng et al., 2024). Static and heavyweight cryptographic protocols suffer from significant latency (Din et al., 2025; Fang et al., 2020), energy consumption (Chaganti, 2025), and may be vulnerable in key management (Fang et al., 2020). As a result, IoT security research seeks lightweight protection and detection mechanisms. The central challenge in this landscape is the transition from perimeter security to zero-trust models, where every entity must continuously prove its integrity and trustworthiness. Privacy encompasses protecting user identity, preventing personal inferences, and ensuring regulatory compliance. IoT devices continuously collect data about individuals, environments, and activities, often without the user’s explicit knowledge. Consequently, privacy risks arise. Protection mechanisms such as data anonymization, differential privacy, encryption, and access control can be used to limit exposure. However, deploying these in IoT requires careful consideration. For example, when IoT systems are deployed by static role-based policies, access rights cannot adapt as IoT environment changes. Besides, cryptography also poses challenges in key lifecycle management. Informed consent, data ownership, and accountability become harder to sustain across multi-party, cross-layer data flows. Accordingly, recent research integrates trust-based differential-privacy-based mechanisms (Nawshin et al., 2024), federated learning (FL) for sensitive data (Khan et al., 2025b), and privacy-preserving access control (Gheisari et al., 2025). The central challenge in this landscape is the transition from data secrecy to context-aware privacy preservation in order to balance data utility with user privacy. 2.1. IoT System Characteristics Securing IoT deployments is inherently difficult: devices are heterogeneous and resource-constrained, adversaries may be external or already authenticated, and privacy must be preserved while data is continuously sensed, transmitted, and processed. The literature characterizes IoT systems along several complementary dimensions. IoT systems are commonly described through functional layers. These are typically physical (perception), network, and application layers (Sun et al., 2025; Tyagi et al., 2023; Alhandi et al., 2023; Konsta et al., 2022), with extended variants with processing or transport layers (Chandrasekaran and Rajasekaran, 2024). IoT systems can also be characterized by the location of sensing, computation, and decision-making along the device–edge–fog–cloud continuum. These are device-level (Turkina and Ihnatiev, 2020; Lakhan et al., 2024), edge-level, fog-level (Rahman et al., 2023), cloud-level (Kumari et al., 2020) and device–edge–cloud combinations (Souri et al., 2024; Sun et al., 2024; D’aniello and Fotia, 2025; Messina et al., 2025a). Third, IoT systems can be further distinguished by their application domains. In order to reflect requirements of the deployed systems, these could be consumer, enterprise, commercial, industrial, or military application domains. For example, (Nashiruddin and Hidayati, 2019) investigates IoT application domains under two: critical IoT and massive IoT. Additional dimensions could be the organizational architecture of IoT entities, such as centralized, hierarchical, or peer-to-peer structures, as well as node mobility, ranging from static infrastructure to mobile vehicular and aerial networks (Dong et al., 2023; Zhang et al., 2024a; Chen et al., 2024). For this survey paper, we consider edge-enabled IoT architectures based on a three-layer model and four types of application domains, as presented in Figure 1. We divide IoT application domains by end-user and market as Business-to-Consumer (B2C) and Business-to-Business (B2B). The B2C segment includes consumer IoT which encompasses devices and ecosystems designed for direct use by individuals to improve quality of life, convenience, and personal wellness. The B2B segment is significantly more diverse, focusing on enterprise-level applications where IoT is deployed to optimize processes, reduce costs, and manage assets. It includes commercial, industrial, and infrastructure IoT. Figure 1. Three-Layer Model of IoT Systems Across Consumer, Commercial, Industrial, and Infrastructure DomainsThree-Layer Model of IoT Systems Across Consumer, Commercial, Industrial, and Infrastructure Domains 2.2. Trust in IoT The trust landscape defines who can securely and reliably operate on behalf of whom, encompassing trust in devices, the data they produce, and the services that act upon that information. It determines not only who is allowed to participate in the system, but also how much autonomy each participant is granted under varying conditions. Many researchers associate trust management solely with social IoT environments (Moeinaddini et al., 2025; Narang and Kar, 2021; Latif et al., 2022; Magdich et al., 2022). In reality, devices in all IoT systems depend on many others for data and services. A well-known example of this occurred in February 2020 when Simon Weckert artificially created a virtual traffic jam on Google Maps by transporting 99 smartphones in a wheelbarrow (Marche and Nitti, 2020). Although not a conventional IoT deployment, this incident shows how untrusted data can propagate through a system and lead to incorrect operational decisions. To better understand trust in IoT operations, it is first necessary to establish key trust-related definitions and then examine how trust management is performed in IoT systems. Trust management schemes identify three participant roles: the trustor, who evaluates and holds trust; the trustee, whose trustworthiness is assessed; and the recommender, who supplies indirect evidence about a third party (Khoshvaght et al., 2025). These roles provide the basis for defining the key concepts underlying trust assessment in IoT. We additionally use the following definitions: Definition 1: Trustworthiness refers to the degree to which a trustee exhibits observable qualities such as reliability, integrity, competence, and benign behavior. (Tsekenis et al., 2025) further incorporates factors such as capacity, energy, performance, and cost. In general, trustworthiness denotes the inherent properties of the trustee that justify a given trust assessment. Definition 2: Trust is the quantitative or qualitative measure of the confidence that a trustor a places in a trustee x to behave as expected over interval [t0,t][t_0,t]. Formally, we can define trust as in Equation 1 where Ea(x)E_a(x) denotes the trust evidence available to a, and f⁡(⋅)f(·) is the trust evaluation function. (1) Ta(x∣t)=f⁡(Ea(x)),T_a(x t)=f\! (E_a(x) ), Definition 3: Device trust is the value Ta(x∣t)∈[0,1]T_a(x t)∈[0,1] by which trustor a expresses confidence that trustee device x will operate benignly up to time t, computed from evidence EaE_a about d’s behavioral history, operational integrity, and adherence to expected node-level functions. Definition 4: Data trust is the graded confidence Ta(x∣t)T_a(x t) by which trustor a judges that trustee data x is reliable for use, based on evidence Ea(x)E_a(x) about its accuracy, integrity, or freshness. Definition 5: Service trust is the graded confidence Ta(x∣t)T_a(x t) by which trustor a judges that service x∈x _x (all services of a specific IoT device) will deliver its advertised functionality correctly, completely, and within agreed quality-of-service over interval [t0,t][t_0,t], without malicious or negligent behavior, based on service-specific evidence Ea(x)E_a(x). Definition 6: A trust relationship is a measurable, subjective, and transferable association (Yang et al., 2023) between a trustor and a trustee, representing the trustor’s context-dependent confidence in the trustee. Definition 7: Trust-related attacks are a class of adversarial strategies in which malicious entities deliberately manipulate the inputs, computation, or dissemination of trust scores to subvert the trust management system itself. Unlike conventional network attacks that target data or availability, trust poisoning attacks manipulate or corrupt the evidence upon which trust assessments are built. In order to develop trust relationships and protect against adversarial behaviors and cyber-physical attacks, trust must be managed continuously. Figure 2 illustrates the conceptual workflow of trust management process in IoT. Trust management begins with evidence acquisition, in which a trustor may collect device, data, and service related information that serve as inputs to trust assessment. These evidences are then processed during evidence aggregation, where an initial trust value is formed, direct and indirect evidence may be aggregated, and normalized. After that, a trust modeling technique (rule-based, probabilistic, ML, or blockchain-assisted) computes a trust value per targets in trust computation step. Subsequently, trust distribution determine where trust is calculated, stored, and shared across the network. For example, at an edge device these trust values can be stored and how updated values are propagated to other entities. Trust scores can be then used by IoT end devices, edge nodes or cloud/data center to support decisions in IoT operations such as routing, access control, service selection, and intrusion detection. Finally, in trust maintenance trust scores are updated after new interactions or elapsed time. Figure 2. Conceptual Trust Management ProcessConceptual Trust Management Process Most IoT trust management frameworks follow a common process, as illustrated in Figure 2. Before discussing current frameworks, it is important to briefly discuss some important components of this process. 2.2.1. Trust Evidence Acquisition It is a process that an IoT system uses to gather information about IoT entities. Some surveys distinguish single and multi attribute evidence collection (Sagar et al., 2024; Kamble et al., 2024; Alhandi et al., 2023; Konsta et al., 2022). We omit this distinction because most state-of-the-art frameworks use multiple attributes; instead we classify evidence acquisition by source: direct or indirect. Direct evidence refers to trust information based on an assessing entity a’s first-hand observations of a target x, which may be a device, data item, stream, or service. Let τ⁡(x)∈device,data,serviceτ(x)∈\device,data,service\ denote the trust target type of x. Direct evidence is represented by a pair of positive and negative outcomes, (pa,x,na,x)(p_a,x,n_a,x), and a vector of normalized metrics ma,x=(ma,x(1),…,ma,x(L))m_a,x=(m_a,x^(1),…,m_a,x^(L)), where each ma,x(ℓ)∈[0,1]m_a,x^( )∈[0,1]. The direct trust score is then computed uniformly as in Equation 2 where Φ⁡(⋅) (·) is an aggregation function whose inputs are interpreted according to τ⁡(x)τ(x). (2) Td(a,x)=Φ⁡(pa,x,na,x,ma,x,τ⁡(x))T_d(a,x)= \! (p_a,x,\;n_a,x,\;m_a,x;\;τ(x) ) In practice, this reduces to either a success ratio as shown in Equation 3: (3) Td(a,x)=pa,xpa,x+na,xT_d(a,x)= p_a,xp_a,x+n_a,x or a weighted metric combination as given in Equation 4: (4) Td(a,x)=∑ℓ=1Lωℓ(τ⁡(x))⋅ma,x(ℓ),∑ℓ=1Lωℓ(τ⁡(x))=1T_d(a,x)= _ =1^L _ \! (τ(x) )· m_a,x^( ), _ =1^L _ \! (τ(x) )=1 Direct evidence can be further divided into two forms, as shown in Figure 5: experience and observations. Nodes’ direct interactions with each other provide experience-based evidence, such as recording the number of successfully forwarded packets relative to failed ones (Osamy et al., 2025). Observation-based evidence, on the other hand, is obtained through monitoring or overhearing interactions between other nodes. Direct evidence acquisition is generally considered more objective observation from the perspective of an IoT node. It is less dependent on other’s subjective observations, therefore it is less vulnerable to trust poisoning attacks. Although indirect evidence is more susceptible to trust-poisoning attacks, it becomes essential when direct interactions are unavailable. In such cases, trust is inferred from recommendations or reputation (consensus). Recommendations represent evaluations shared by neighboring nodes regarding the reliability of a target entity (Marche and Nitti, 2020). In contrast, reputation (consensus) aggregates trust opinions from multiple nodes into a community-level score. However, its effectiveness depends on the credibility of obtained trust value (Yang et al., 2023) and recommenders (Ahmed et al., 2022). The set of all indirect source can be denoted as ℛxR_x, and each source r providing opinions about target x. mr(x)∈[0,1]m_r(x)∈[0,1] denotes the normalized trust opinion that r contributes about x. Indirect trust can be computed as in Equation 5 where ωr∈[0,1] _r∈[0,1] is the credibility weight assigned to source r: (5) Tr(x)=∑r∈ℛxωrmr(x)∑r∈ℛxωrT_r(x)= _r _x _r\,m_r(x) _r _x _r In hybrid trust evidence acquisition models, direct and indirect evidence are combined to obtain an aggregated trust score. The trust that assessing entity a assigns to target x is commonly computed as a weighted combination (Wang et al., 2025a) where α∈[0,1]α∈[0,1] controls the relative influence of direct versus indirect evidence: (6) T⁡(a,x)=α⋅Td(a,x)+(1−α)⋅Tr(x)T(a,x)=α· T_d(a,x)+(1-α)· T_r(x) This hybrid formulation is motivated by a common limitation of IoT deployments: direct evidence is often incomplete, intermittent, or outdated by the time a stable trust estimate can be formed. IoT nodes try to collect signals related to competence, honesty, and security (Bangui et al., 2025). However, building a reliable interaction history remains a significant challenge (Messina et al., 2025b). When first-hand interaction history is limited, schemes typically reduce weight α and rely more heavily on recommendations or reputation (Sharma et al., 2024; Guo et al., 2022a); as sufficient direct observations accumulate, Td(a,x)T_d(a,x) is given greater weight and dependence on potentially unreliable indirect sources is reduced. On the other hand, another challenge is that IoT environments often change faster than the evidence base used to support trust decisions (Khan et al., 2025b; Yu et al., 2023). Sparse interactions therefore yield insufficient evidence for stable trust relationships. To compensate, some studies incorporate interaction intensity as an additional trust parameter (Sharma et al., 2025), while (Marche and Nitti, 2020) introduces a relationship component that assigns the highest value to directly connected entities and, for indirect pairs, derives trust from the weakest link along the available social path. However, if sufficient direct observations and trustworthy recommendations are both unavailable, many existing schemes simply assign a default initial trust score to previously unknown entities, which may lead to inaccurate trust decisions during early interactions (Huang et al., 2025). 2.2.2. Trust Computation Trust computation maps aggregated evidence to a trust value for a given target, such as a device, data, or service. In the state-of-the-art, there are many families of computation methods, each differing in how evidence is interpreted, combined, and converted into a trust value. Rule-based and weighted-sum models combine selected trust inputs using predefined rules, weights, thresholds, or ranking methods. They are generally lightweight and interpretable, but their effectiveness depends on how the rules and weights are defined (Bampatsikos et al., 2025; Ali et al., 2024). Fuzzy-logic models use expert-defined rules to handle imprecise inputs and avoid relying on strict thresholds (Rehman et al., 2025). Probabilistic and statistical approaches model trust as uncertain or evolving evidence and update it as new observations become available. Examples include Beta-based reputation, Bayesian updating, and Markov models (Alsheakh and Bhattacharjee, 2020). Subjective logic further represents belief, disbelief, and uncertainty separately, rather than reducing them to a single value. Learning-based approaches, including neural networks (N), clustering, and federated learning, learn trust patterns from behavioral or interaction data and can capture relationships that are difficult to express through predefined rules (Moeinaddini et al., 2025; Zhang et al., 2021; Hammad and Trilles, 2025). Agent-based designs also appear as multi-agent reputation and clustering schemes. They treat IoT nodes as cooperating agents whose group membership follows local trust (Fortino et al., 2023), while multi-agent reinforcement learning uses interacting learners to update trust-aware decisions at runtime (Tuncel and Oztoprak, 2025; Chen et al., 2023). However, learning based methods generally introduce higher data and computational requirements. Game-theoretic approaches model trust-related interactions as strategic behavior between entities and can incorporate mechanisms such as voting, incentives, and coalition formation (Moudoud et al., 2022). Blockchain is also used in trust computation frameworks, mainly to provide a distributed mechanism for recording, sharing, and protecting trust values rather than serving as a trust computation method by itself (Latif et al., 2023; Sinha et al., 2024). No single approach is universally preferred: the choice depends on the trust target (device, data, or service), the available evidence, and the resource constraints of the deployment (Akli and Chougdali, 2023). Besides, authors in (Bampatsikos et al., 2025) highlight the need for trust modeling while considering which IoT domains it will be adapted to. 2.2.3. Trust Distribution Trust distribution is the stage at which computed scores are stored and shared so that other entities can use them for operational decisions. Before scores can be disseminated, they must be represented in a comparable form. In the state-of-the-art, trust may be modeled as a binary, nominal, discrete, or continuous value (Akli and Chougdali, 2023). Most schemes use a scalar score in [0,1][0,1], while others keep multiple trust dimensions or map entities to discrete states such as trustworthy or malicious (Ismail et al., 2024; Kuppusamy and Murugesan, 2024; Khan et al., 2025b; Kumar and Sharma, 2025; Wang et al., 2024; Ali et al., 2024). Trust can also be represented as a vector, a matrix, or a graph of relationships, which enables indirect inference when only partial evidence is available (Messina et al., 2025a; Rajendran and Jebakumar, 2022). Once a representation is chosen, two further questions arise: where trust scores are stored, and how they are disseminated across IoT network. Storage may be local to the evaluating node, held at an edge or fog gateway, or recorded on a shared ledger. Dissemination then determines who receives those stored values and along which path: top-down, horizontally among peers, through cluster heads, or by consensus exchange. IoT trust management can be implemented through centralized, distributed, or decentralized architectures. Among the surveyed studies, decentralized architectures are our focus. Table 2 compares the three families in terms of computation placement, dissemination strategy, typical use cases, and key drawbacks. Table 2. Architectural Comparison of Trust Management Placement Architecture Computation Dissemination Best Suited For Key Drawback Decentralized (Our Focus) Edge compute locally; cloud syncs globally Local gateway push; regional cloud coordination Large-scale, mixed-resource IoT (e.g., IIoT, smart cities) Complex gateway management Distributed Peer evaluation without central coordination P2P broadcast or consensus protocols Dynamic, ad-hoc networks (e.g., VANETs) High resource overhead on constrained sensors Centralized Central server processes all raw telemetry, compute trust Cloud broadcasts lists/policies to nodes Static, low-power networks with stable links Single point of failure; high latency In centralized architectures, central controller collects interaction evidence, computes trust, and disseminates the result top-down through blacklists, trust tokens, or policy updates. This model simplifies management and benefits from global visibility, but it introduces latency, a single point of failure, and scalability bottlenecks when all raw telemetry must be uploaded for analysis (Xiong et al., 2023). In decentralized architectures, computation is shifted to intermediate nodes such as edge gateways, fog brokers, or cluster heads. These nodes evaluate trust locally, aggregate neighborhood reputation, and share summaries horizontally with peer managers while synchronizing with upper tiers.. In distributed architectures, there is no fixed trust coordinator: individual nodes evaluate peers directly and propagate updates through broadcast or consensus protocols. This supports ad-hoc and vehicular environments, but it imposes additional communication and computation costs on constrained devices. 2.2.4. Trust Maintenance Trust assessments made at the beginning should not be considered permanent. Trust must be maintained via update triggers and decay factors. Updates can be executed in three options. Event-driven updates trust values quickly upon an event, such as a packet exchange, service invocation, or incoming recommendation. For instance, (Latif et al., 2022) uses event-driven updates to maintain trust ratings after every client-server communication. Time-driven updates refresh scores at predetermined intervals, which is straightforward to manage. However, it may result in stale trust between updates. Hybrid methods integrate both techniques, updating on interaction when evidence is available and periodically updating otherwise (Li et al., 2024b; Ahmad et al., 2025). Although frequently overlooked, trust decay is essential for history-based frameworks, as maintenance remains fundamentally incomplete without a temporal aging mechanism: fresh occurrences update the score, but old evidence may still dominate the outcome. Decay mechanisms discount older data to prioritize current conditions over past history. This is important in IoT because devices may become inactive, environments change rapidly, and attackers may exploit outdated high trust through on-off behavior. Decay is often implemented using forgetting factors such as sliding time windows, or separate trust terms. (Sharma et al., 2025) includes a recent trust in their suggestion, whereas (Kumar and Sharma, 2025) uses historical trust, where the final trust value is based on the current trust and the average of previous trust values. 2.3. Trust-Related Attacks In the literature, several taxonomies have been proposed to categorize trust-related attacks. (Tyagi et al., 2023) employs a basic taxonomy: internal, external attacks and their intersection. In addition to classification based on attacker location, (Hossain et al., 2024) suggests a taxonomy of three more classes: device specification, information damage level, and host compromise. Both (Sagar et al., 2024) and (Marche and Nitti, 2020; Moeinaddini et al., 2025) use two-dimensional classifications: the former categorizes attacks as individual or collusive, while the latter groups them by target (service/recommendation) and impact scope (single/group). Table 3. Classification of Trust-Related Attacks by Target and Architectural IoT Layer Target IoT Layer Attack Type Abbr. Device Trust Network/Application Sybil Attack SA Physical Node Capture NC Physical Sleep Deprivation SDA Data Trust Application Bad-mouthing BMA Application Good-mouthing (Ballot-stuffing) GMA Application Self-promoting SPA Application Data Poisoning DP Physical/Network False Data Injection FDI Physical/Network Jamming JA Physical Sensor Spoofing S Service Trust Application Opportunistic Service OSA Application Discriminatory Attack DA Application Whitewash Attack WA Application Malicious with Everyone Attack ME Network/Application On-Off Attack OOA Network Selective Forwarding SFA Network Black-hole BHA Network Gray-hole GHA Moreover, (Yang et al., 2025) focuses specifically on data trust-related attacks and proposes a taxonomy based on three dimensions: attack methodology, attacker type (non-strategic vs. strategic), and attack scope (local vs. network-wide). While their approach may provide understanding of adversarial behavior, it only addresses trustworthiness of information within navigation systems. Finally, while (Konsta et al., 2022) maps trust attacks strictly to single IoT layers, our proposed taxonomy allows for hybrid layer assignments. As shown in Table 3, we classify each threat by its architectural layer and specific trust target (device, data, or service). Furthermore, Table 4 maps the same attacks onto the process in Figure 2. It records where the compromise occurs, its primary effect, whether the attack is internal or external, the behavior of attacker, and how difficult the attack is to detect. Table 4. Trust-Related Attacks Across The Trust Management Process in Figure 2 Abbr. Targeted Trust Management Process Primary Effect Attack Origin Attacker Behavior Detection Difficulty SA Evidence flow; Trust flow; Maintenance Fake identities bias evidence and disseminated scores I & E Persistent ■■■ NC Evidence flow A captured node becomes a false evidence source I & E Persistent ■■□ SDA Evidence flow Honest nodes are exhausted and stop reporting I & E Persistent ■■□ BMA Acquisition False recommendations slander honest nodes I Persistent ■■□ GMA Acquisition; Trust flow Colluding nodes inflate shared reputation I Strategic ■■■ SPA Acquisition; Trust flow A node inflates the score that others receive about it I Persistent ■■□ DP Evidence flow; Computation Corrupted samples bias the trust model itself I & E Persistent ■■■ FDI Evidence flow; Aggregation False observations enter the evidence stream I & E Persistent ■■□ JA Evidence flow; Trust flow Evidence collection and score dissemination are disrupted E Persistent ■□□ S Evidence flow Fabricated sensor readings replace genuine measurements E Persistent ■■□ OSA Trust-driven decision flow Service is degraded after the node has been selected I Strategic ■■■ DA Trust-driven decision flow Only selected requesters receive poor service I Intermittent ■■■ WA Maintenance Identity reset discards prior distrust I Strategic ■■■ ME Trust-driven decision flow Every trust-based request is served maliciously I Persistent ■□□ OOA Trust-driven decision flow; Maintenance Alternating behaviour keeps the node selectable I Intermittent ■■■ SFA Trust-driven decision flow Trusted forwarding is applied only to some packets I Persistent ■■□ BHA Trust-driven decision flow Trusted routing delivers traffic that is then dropped I Persistent ■□□ GHA Trust-driven decision flow Trusted routing is violated only part of the time I Intermittent ■■■ I = Internal Attack, E = External Attack, I & E = Both Applicable Table 4 reveals that trust-related attacks can compromise the trust management process at multiple stages rather than targeting trust computation alone. Evidence-related attacks such as S, FDI, and BMA primarily corrupt the information used to establish trust, whereas OSA, DA, SFA, BHA, and GHA exploit trust-based decisions after a node has already been considered trustworthy. Attacks such as SA, DP, and OOA are particularly broad in their impact, spanning multiple stages of the process and potentially influencing both trust assessment and subsequent decisions. There is also a correlation between attacker behavior and detection difficulty. Persistent attacks generally leave continuous behavioral evidence and are therefore comparatively easier to detect, while intermittent and strategic attacks, including OOA, GHA, DA, GMA, OSA, and WA, deliberately preserve or recover favorable trust levels and are consequently harder to identify. This is especially evident for attacks that manipulate trust without continuously violating expected behavior. Overall, this representation shows that attack resilience should be evaluated at the process level, since a trust mechanism may accurately compute trust from its inputs yet remain vulnerable when those inputs, trust-based decisions, or subsequent trust updates are manipulated. 3. Review Methodology To provide a comprehensive assessment of the current state of trust management in IoT, we adopt a systematic literature review approach. The primary repositories selected for this study are Web of Science (WoS) and Scopus. Constructed search queries given in Figure 3. Database: Web of Science (WoS) Date Range: 2020–2026 ("Trust Management" OR "Trust Evaluation" OR "Trust Model" OR "Trust Mechanism" OR "Reputation System" OR "Behavior-based Trust") AND ("IoT" OR "Internet of Things" OR "IoT device*" OR "edge-enabled IoT" OR "edge-assisted IoT") AND ("edge-ai" OR "ai" OR "Artificial Intelligence" OR "ml" OR ("agent" AND "learning")) AND (Publication Date: 2020/2027) NOT ("survey") NOT (Document Type: Review) AND Language: English (a) Database: Scopus Date Range: 2020–2026 (TITLE-ABS-KEY("Trust Management" OR "Trust Evaluation" OR "Trust Model" OR "Trust Mechanism" OR "Reputation System" OR "Behavior-based Trust")) AND (TITLE-ABS-KEY("IoT" OR "Internet of Things" OR "IoT device*" OR "edge-enabled IoT" OR "edge-assisted IoT")) AND (TITLE-ABS-KEY("edge-ai" OR "ai" OR "Artificial Intelligence" OR "ml" OR ("agent" AND "learning"))) AND (PUBYEAR > 2019 AND PUBYEAR < 2027) AND NOT TITLE-ABS-KEY("survey") AND NOT DOCTYPE(re) AND ( LIMIT-TO ( LANGUAGE,"English" ) ) (b) Figure 3. Search Queries Applied to the Two Indexing Databases: (a) Web of Science (WoS) , and (b) ScopusSearch Queries The review technique follows PRISMA framework to promote reproducibility and transparency in the article selection process. Figure 4 illustrates the steps involved in the literature screening and selection process. Research included in qualitative synthesis are examined in steps (i) scope identification, (i) trust-design evaluation, and (i) operational and attack coverage. PRISMA Flow Diagram Figure 4. PRISMA Flow Diagram From Identification To Screening, Eligibility, And Inclusion StepsPRISMA Flow Diagram Step 1: Scope identification Each state-of-the-art study is first assigned to one of the four IoT application domains considered in this survey: Consumer, Commercial, Industrial, or Infrastructure IoT. This classification is based on the primary operational context and type of IoT system investigated in the study. The study is then positioned according to the IoT layer at which trust is primarily computed, updated, or enforced, using the three-layer model of physical, network, and application layers. When trust mechanisms span multiple layers, a hybrid placement is recorded. This two-level classification establishes both the application context and architectural location of the trust mechanism. Step 2: Trust-design evaluation Each study is further classified according to the five orthogonal trust-design dimensions presented in Figure 5. These dimensions capture what is trusted, how trust is computed, which evidence is used, how trust is updated or maintained. This stage enables the comparison of the underlying design choices. Trust Design Taxonomy Figure 5. Trust-Design Taxonomy for Classifying Trust Management SchemesTrust Design Taxonomy Step 3: Operational and Attack Coverage The operational roles of trust are examined and results are given in Table 11. For each study, we record how trust is used to securely guide IoT operations, such as routing, service selection, access control, data aggregation, or intrusion detection. The attacks reported as mitigated by each study are then extracted from the original paper and categorized according to the attack taxonomy in Table 12. 4. Current IoT Trust Based Frameworks We examine state-of-the-art trust management frameworks using the four-step classification from Section 3. The comparative taxonomies are organized by IoT application domain (consumer, commercial, industrial, infrastructure) and ordered top-down by operational layer (application to physical). Cross-layer mechanisms are explicitly indicated. Notably, most surveyed literature targets the application and network layers, where node cooperation and interaction primarily occur. The distribution respect to application domains is balanced across the four domains, although some years have limited coverage in specific domains as given in Figure 6. Distribution Across IoT Application Domains (a) Distribution Across IoT Application Domains (b) Figure 6. Surveyed Literature Overview: (a) Distribution Across IoT Application Domains, and (b) Annual Distribution By Domain From 2020 to 2025 Table 5. Comparative Taxonomy of Trust Management in Consumer IoT Research Scope Trust Design Evaluation & Security Ref. (Year) Evaluation Layer Trust Target Computation Model Trust Inputs Update Attacks Mitigated Validation Code? (Fortino et al., 2023) (2022) Application Device & Service Weighted feedback aggregation, clustering, multi-agent Peer feedback, resource relevance, interaction frequency-recency, trustor-prior reputation E Collusion, GMA, SPA, BMA, OOA, OSA Simulation No (Mahmood et al., 2025b) (2025) Application Device Logarithmic trust function, time-gated update Application transactions, authentication, timing gap, recommendations E & T GMA, BMA, DoS/DDoS, flooding, SA, replay, JA, cloning Simulation on Cooja, vs LightTrust, ETES No (Moeinaddini et al., 2025) (2025) Application Service MLP, FL Direct, recommendations E OOA, WA, OSA, BMA, GMA, SPA, DA, ME Simulation on Python No (Rehman et al., 2025) (2025) Application Device Hybrid fuzzy logic, FL Sensor data, user behavior, context, historical interactions, real-time feedback E & T OOA, WA, DDoS, GMA, BMA Simulation on MATLAB, vs MetaCIDS, MetaverseAuth, TrustITS Available upon request (Kuppusamy and Murugesan, 2024) (2024) Application & Network Device & Data Direct, recommended, belief-based trust with threshold classification, clustering, RNN classifier Direct one-hop behavior, recommendations, belief trust, reputation from network features T External attacks, double-spending, collusion Experimental evaluation on Python Available upon request (Le and Shetty, 2022) (2022) Application & Network Device & Data GAN, VAE, FL, blockchain consensus, reputation/data-similarity trust Historical interactions, neighbor opinions, data similarity, signal strength, voting, social trust, computation contribution – SA, DP, FDI, eavesdropping, OSA Conceptual, healthcare HAR use-case No (Zhang et al., 2021) (2021) Application & Network Device & Data Feedforward N, blockchain (Merkle-tree trust archiving) 18 N inputs (vehicle/incident location, visibility distance, current trust, RSU range, traffic direction), message context, LTL/GTL trust E & T SA, BMA, OOA, fake/tampered messages, compromised RSU Simulation on SUMO, NS-2 No (Alsheakh and Bhattacharjee, 2020) (2020) Network Device Explainable Bayesian trust scoring with severity-aware weighting Service-access matches, access violations, access uncertainty, cumulative uncertain volume, access violation diversity, MUD/ACL baselines T DDoS, reflection attacks, flooding, malware Experimentation on real PCAP datasets (UNSW, CTU), SVM threshold classification No (Bampatsikos et al., 2025) (2025) Network Device 2D Markov chain, weighted-sum trust score, AHP/TOPSIS CPU/RAM usage, security level (TEE/PUF RoT), OS/package age, packet loss, risk/reputation, vulnerability index, IDS score E DDoS, NC, FDI, GMA, BMA, SPA, APTs Real-time testbed No (Moudoud et al., 2022) (2022) Network Device & Data HMM behavior prediction, stake-based voting game HMM global trust, neighbor recommendations, past interactions, stake/deposit, vote validity, data skewness E & T FDI, single-point-of-failure Simulation on Python, FDI dataset; compared with LSTM, GRU, RSS, Sec5G No (Ali et al., 2024) (2024) Network & Physical Device & Data Weighted direct and indirect trust with sigmoid function, threshold classification GTS-request patterns, received vs requested packets, channel capacity, neighbor trust E & T Slot-capturing DoS, impersonation, FDI Simulation, vs FCFS, R, SJF, LJF, No (Ahmed et al., 2025) (2025) Physical Device FL, feed-forward ANN, Z-score/MAD outlier detection RSSI, LQI, internal temperature, battery level, MAC, radio channel, antenna orientation T S, replay, SA, DoS Zigbee Z1 testbed dataset (347,200 instances) No E = Event-driven Update, T = Time-driven Update, E & T = Hybrid, - = Not Reported To start with, Table 5 lists the investigated IoT trust frameworks in consumer IoT application domain. The main goal in consumer IoT is to make everyday connected systems more dependable without sacrificing usability in general. In this domain, a large share of this research focuses primarily on device trust (Mahmood et al., 2025b; Rehman et al., 2025; Alsheakh and Bhattacharjee, 2020; Bampatsikos et al., 2025; Ahmed et al., 2025), while service trust and data trust receive comparatively less attention. A second group assess trustworthiness of data together with device trust (Kuppusamy and Murugesan, 2024; Le and Shetty, 2022; Ali et al., 2024; Moudoud et al., 2022; Zhang et al., 2021). Only (Fortino et al., 2023) jointly considers service trust and device trust, and (Moeinaddini et al., 2025) is the only research we surveyed that addresses service trust alone in this application area. The choice of trust inputs closely follows the usage of which trust target in evaluation layer. Physical-layer schemes such as (Ahmed et al., 2025) use link and device-state signals and therefore focus on device trust. Network-layer works including (Alsheakh and Bhattacharjee, 2020; Bampatsikos et al., 2025) assess device trust from communication and operational evidence, while (Moudoud et al., 2022; Ali et al., 2024) extend this to data trust when integrity-related inputs such as data skewness or received-versus-requested packets are added. Application-layer studies such as (Mahmood et al., 2025b; Rehman et al., 2025) mainly target device trust using transactions, authentication, and contextual analytics; (Fortino et al., 2023) and (Moeinaddini et al., 2025) are the main exceptions that explicitly consider service trust. Cross-layer schemes (Kuppusamy and Murugesan, 2024; Le and Shetty, 2022; Zhang et al., 2021) evaluate device and data trust simultaneously. (Kuppusamy and Murugesan, 2024; Le and Shetty, 2022) are both in the healthcare field, while (Zhang et al., 2021) uses trust management specifically for vehicular networks. (Kuppusamy and Murugesan, 2024) recommends using direct one-hop behavior, neighbor recommendations, and belief-based reputation from network features as trust inputs. Furthermore, these are aggregated with threshold rules and clustering before trust gates access to electronic health records. Similarly, (Le and Shetty, 2022) uses neighbor opinions, signal strength, voting, and data-similarity signals as inputs, then combines FL, GAN/VAE sanitization, and blockchain-supported reputation to judge health-activity data trust. In (Zhang et al., 2021), authors feed location, mobility, RSU range, message context, and prior local/global trust inputs into a feedforward neural network, while blockchain stores updated trust values for later validation of traffic and incident messages. Computation models in consumer IoT fall into three overlapping groups. The largest group applies weighted aggregation of direct and indirect evidence (Fortino et al., 2023; Mahmood et al., 2025b; Kuppusamy and Murugesan, 2024; Bampatsikos et al., 2025; Ali et al., 2024). A second group uses probabilistic or statistical inference (Alsheakh and Bhattacharjee, 2020; Bampatsikos et al., 2025; Moudoud et al., 2022), and a third learns the mapping from behavior to trust (Kuppusamy and Murugesan, 2024; Moeinaddini et al., 2025; Rehman et al., 2025; Le and Shetty, 2022; Zhang et al., 2021; Ahmed et al., 2025). The groups differ mainly in how the score is shaped and what it estimates. Within weighted aggregation, (Mahmood et al., 2025b) applies a logarithmic function so that trust accumulates slowly, whereas (Ali et al., 2024) applies a logistic function that sharpens the decision boundary, and (Fortino et al., 2023) places the security property in the weights themselves by scaling feedback with resource relevance, interaction frequency, and trustor reputation to resist collusion. Weights are otherwise hand-tuned, and (Bampatsikos et al., 2025) is the only study deriving them formally through AHP and TOPSIS. In the probabilistic group, (Alsheakh and Bhattacharjee, 2020) scores past behavior through Bayesian updating with severity-aware discounting, while (Bampatsikos et al., 2025; Moudoud et al., 2022) predict future behavior through Markov and hidden Markov models, which is valuable where interaction history is short and sparse. Where learning is used, the architectures are typically small feed-forward networks over low-dimensional feature vectors (Moeinaddini et al., 2025; Zhang et al., 2021; Ahmed et al., 2025), whereas deeper generative and recurrent models appear only in (Le and Shetty, 2022; Kuppusamy and Murugesan, 2024). Only (Rehman et al., 2025) adds fuzzy inference with fractional-order dynamics for stability guarantees, and only (Moudoud et al., 2022) makes honest participation explicit through stake-based voting, while subjective logic and evidence-theoretic fusion are absent. Earlier works rely on Bayesian scoring, neural inference, and blockchain reputation, whereas federated designs primarily in 2025. Federated and edge-local training is therefore the most recent trend in this domain, consistent with the privacy sensitivity of consumer-related evidence. Among the studies in Table 5, BMA (Fortino et al., 2023; Mahmood et al., 2025b; Moeinaddini et al., 2025; Rehman et al., 2025; Zhang et al., 2021; Bampatsikos et al., 2025) and GMA (Fortino et al., 2023; Mahmood et al., 2025b; Moeinaddini et al., 2025; Rehman et al., 2025; Bampatsikos et al., 2025) are the most common, together with OOA (Fortino et al., 2023; Moeinaddini et al., 2025; Rehman et al., 2025; Zhang et al., 2021). Among conventional threats, SA (Mahmood et al., 2025b; Le and Shetty, 2022; Zhang et al., 2021; Ahmed et al., 2025), DDoS (Mahmood et al., 2025b; Rehman et al., 2025; Alsheakh and Bhattacharjee, 2020; Bampatsikos et al., 2025), and FDI (Le and Shetty, 2022; Bampatsikos et al., 2025; Moudoud et al., 2022; Ali et al., 2024) receive the most attention, while other attack classes appear only rarely. Routing attacks such as blackhole, grayhole, and selective forwarding are largely absent from Consumer IoT studies, indicating that they are treated as more relevant to multi-hop networking domains such as Industrial IoT or Infrastructure IoT. Table 6. Comparative Taxonomy of Trust Management in Commercial IoT Research Scope Trust Design Evaluation & Security Ref. (Year) Evaluation Layer Trust Target Computation Model Trust Inputs Update Attacks Mitigated Validation Code? (Ahmad et al., 2025) (2025) Application Device Behavior-based trust Reputation T SA, OOA, replay, DoS, MiTM Implementation on Raspberry Pi, ESP32 No (Rathee et al., 2025) (2025) Application Device 4-dimensional trust mechanism, blockchain surveillance Sensor behavior, energy consumption, forwarding behavior, activeness, delay, information accuracy T Abnormal device behavior, control-command, coordinated threats Simulation on MATLAB Available upon request (Wang et al., 2025a) (2025) Application Service Weighted trust evaluation Hybrid E BMA, GMA, DA Simulation No (Sun et al., 2024) (2024) Application Service Interval MADM trust evaluation, deviation-maximization weighting QoS attributes via SLO, interval-valued monitoring values E Not reported Case study on QWS dataset No (Latif et al., 2022) (2022) Application Device & Service MCDA (QoS-weighted ratings), incremental SVD collaborative filtering QoS params, peer post-communication ratings E DoS, DDoS, malicious edge nodes, FDI Simulation on EdgeCloudSim, MATLAB No (Liang et al., 2024) (2024) Application Service Blockchain-based reputation, short-term incentives, HRL service optimization Service-provision records, service collaboration frequency, direct/indirect reputation, rewards/punishments, edge resources E & T OSA, unreliable/malicious SP Simulation on Python, PyTorch, train-control case study No (Tsekenis et al., 2025) (2025) Network Device Weighted sum, ML-enhanced genetic algorithm Latency, throughput, capacity, energy efficiency, cost, reliability E & T Not reported Simulation No (Tuncel and Oztoprak, 2025) (2025) Network Device Weighted multi-parameter trust on blockchain, FL K-means clustering, multi-agent RL Direct, indirect, and recent trust T Tampering, eavesdropping, privacy, single-point-of-failure, compromised nodes Simulation on NS-3, vs EDDC, LSTM, GRADE Yes (Sharma et al., 2024) (2024) Network Device & Data Multi-level weighted trust aggregation Direct & Indirect T BMA, GHA, FDI, replay, eavesdropping Simulation with LT-FS-ID dataset No (Chen et al., 2023) (2023) Network Device Reputation-record trust (request-count thresholding), multi-agent DRL Resource request count, task-completion ability under delay constraint, channel state E Resource preemption, interference, JA, data tampering Simulation, Lee/Hata channels, vs KNN, A2C No (Yu et al., 2023) (2023) Network Device & Data Neural network, fuzzy logic Direct/virtual interaction metrics, fuzzy attributes E FDI, malicious terminal detection Simulation on MATLAB with SmartSantander dataset No (Guo et al., 2022a) (2022) Network Device & Data Ground-truth matching, recommender-weighted indirect trust Direct & Indirect E & T FDI Simulation on Python with T-Drive Beijing taxi dataset No (Guo et al., 2022b) (2022) Network Service Delay-based reputation, RPBFT, A3C DRL Service delay, historical reputation, QoS/praise E Byzantine/malicious nodes Simulation on Python, Hyperledger Fabric, vs PBFT, DQN, RD No (Salim et al., 2022) (2022) Network & Physical Device & Data Weighted sum of behavioral metrics Packet/forwarding rates, energy, residual energy, distance, BS feedback T Sinkhole, internal/external attacks, known/chosen-plaintext attacks Simulation on MATLAB No E = Event-driven Update, T = Time-driven Update, E & T = Hybrid, - = Not Reported Secondly, Table 6 lists the investigated IoT trust frameworks in the commercial IoT application domain. Commercial IoT trust work mainly aims to keep enterprise services dependable: filter bad devices, select good services, and support efficient edge/cloud operation. Unlike consumer IoT, the emphasis is less on privacy and more on service reliability, scalability, and institutional trust in business environments. Device trust is still the most popular trust target in commercial IoT, as seen in (Ahmad et al., 2025; Rathee et al., 2025; Tsekenis et al., 2025; Tuncel and Oztoprak, 2025; Chen et al., 2023; Latif et al., 2022; Yu et al., 2023; Guo et al., 2022a; Salim et al., 2022). Similar attention is given to assessing trust considering both device and data (Sharma et al., 2024; Yu et al., 2023; Guo et al., 2022a; Salim et al., 2022). For example, (Guo et al., 2022a) computes data trust as the matching degree between a vehicle’s reported data and UAV-collected ground-truth baselines, updating the score through bounded increase and decrease rules that converge as it approaches its limits. However, data trust is underexplored in this domain. Frameworks assessing service trust (Wang et al., 2025a; Sun et al., 2024; Liang et al., 2024; Guo et al., 2022b; Latif et al., 2022) are more visible than the number of frameworks in consumer IoT. When compared to consumer IoT, a greater attention is given to service trust. It is incorporated in 36% of the surveyed commercial IoT studies, compared with only 17% in Consumer IoT. In the research by (Latif et al., 2022), QoS-driven peer ratings are aggregated into community trust and supports cold-start prediction for new devices. More, (Guo et al., 2022b) applies delay-based service reputation to cross-domain service orchestration and consensus leader selection. Moreover, application layer is the primary deployment point for service trust mechanisms. For example, (Sun et al., 2024) ranks edge/cloud providers using QoS evidence such as availability, latency, and response time through a trust-as-a-service broker, while (Wang et al., 2025a) evaluates edge service collaboration with multi-level trust, whitelist/blacklist control, and reputation attacks such as BMA and GMA. (Liang et al., 2024) further uses blockchain-based service reputation and collaboration records to schedule trustworthy edge services in train-control environments. In contrast, network layer more often evaluates device and data trustworthiness jointly. For example, (Sharma et al., 2024) considers interaction, data, and transmission trust between the members of a cluster, and identity and validation trust at the cluster-head and the base-station levels. They utilized direct observation across a sliding time window and, in the absence of interaction history, from neighbor input. The aggregated score is then used to drive cluster-head selection and routing. Overall, they try to mitigate GHA, OOA, WA, and BMA. Besides (Yu et al., 2023) evaluates terminal trust from interaction accuracy and response quality to detect FDI in smart-city sensing. In (Salim et al., 2022), forwarding behavior is linked with trusted data aggregation to prevent sinkhole attacks in cluster-based networks. (Tsekenis et al., 2025) reframes trustworthiness as a multi-criteria operational metric for 6G mesh node selection using latency, cost, and capacity evidence, but it is not security-oriented and includes no attack model. Consequently, trust inputs such as packet delivery rate (Tuncel and Oztoprak, 2025) forwarding rate and packet success (Salim et al., 2022), latency, cost, and capacity (Tsekenis et al., 2025), channel gain and task completion under delay (Chen et al., 2023) are the trust inputs considered in these groups of frameworks. The trust model in (Tuncel and Oztoprak, 2025) combines direct trust derived from observed node behavior and packet delivery rate with indirect trust obtained from neighboring nodes’ opinions, while recent trust values are further derived from the direct and indirect trust assessments. Table 7. Comparative Taxonomy of Trust Management in Industrial IoT Research Scope Trust Design Evaluation & Security Ref. (Year) Evaluation Layer Trust Target Computation Model Trust Inputs Update Attacks Mitigated Validation Code? (Ratnayake et al., 2024) (2024) Application Data MLP, random forest, KNN Reputation score, SVM decision score E FDI, data manipulation Dataset-based experiments, prototype No (Wang et al., 2025c) (2025) Application Device Weighted sum (direct + indirect) Decryption feedback, interaction outcomes-time-frequency E FDI Formal proofs, JPBC benchmarks, Python simulation with synthetic data No (Cheng et al., 2024) (2024) Application Device Feedback-based trust, Mahalanobis similarity, median filtering Reliability, communication success, node importance, interaction time, feedback E & T BMA, OOA Simulation, vs TFL-DT, LTrust No (Yu et al., 2024) (2024) Application Device Feedback-based weighted trust (direct + indirect) Satisfaction, interaction history, time decay, operation counts, behavioral similarity T BMA, GMA, OOA, Collusion Simulation on NetLogo No (Feng et al., 2023) (2023) Application Device & Service Blockchain-based trust, threshold, majority aggregation Prediction accuracy/timeliness, device feedback, running-state changes, periodic feedback E & T SPA, data tampering, false predictions, BMA Simulation Available upon request (Li et al., 2023a) (2023) Application Device FL with LSTM anomaly detection, weighted trust Consensus participation, behavior detection, traffic anomaly score, historical trust E & T SA, impersonation, insider, collusion Experiments on Hyperledger Fabric/Docker, ZTE traffic No (Kumar and Sharma, 2025) (2025) Network Device SVM, dynamic weighting Payload size, reputation, integrity, network statistics E Spoofing, SA, NC, replay, DDoS Prototype, Contiki-NG, synthetic dataset Available upon request (Zhang et al., 2023) (2023) Network Device & Data Blockchain consensus trust, BLS-based PoRep via VDF Replica proofs, storage capacity, VDF verification, BLS signatures E Storage cheating, data tampering/deletion, TTP risk Experiments on C++/MIRACL library Available upon request (Wang et al., 2024) (2024) Network & Physical Device PKI-based trusted authentication, behavior trust Identity/certificates, platform integrity, network behavior E & T FDI, SFA, replay, eavesdropping, impersonation, MitM Simulation on NS-3 & MATLAB No (Wang et al., 2020) (2020) Physical Device Probabilistic graphical model, approximation algorithm Behavior on Data-collection-communication, direct interactions - Insider, hidden data, malicious/damaged nodes Simulation on MATLAB,NS-3 No E = Event-driven Update, T = Time-driven Update, E & T = Hybrid, - = Not Reported With respect to computation, the commercial IoT literature can be summarized in three groups. The largest group derives trust from weighted, multiple attribute decision making (MADM), or rule-based aggregation of behavioral and QoS evidence (Ahmad et al., 2025; Rathee et al., 2025; Wang et al., 2025a; Sun et al., 2024; Sharma et al., 2024; Salim et al., 2022). A second group employs learning only to optimize a downstream decision, such as offloading, service orchestration, clustering, or topology formation (Chen et al., 2023; Guo et al., 2022b; Liang et al., 2024; Tuncel and Oztoprak, 2025; Tsekenis et al., 2025). A third, much smaller group estimates trust itself with a learned model: (Latif et al., 2022) uses SVD for cold-start prediction, and (Yu et al., 2023) combines a N with fuzzy attributes. Where blockchain is used, it serves as a logging or consensus mechanism (Rathee et al., 2025; Liang et al., 2024; Tuncel and Oztoprak, 2025; Guo et al., 2022b). Three contrasts follow from this grouping. First, relative to consumer IoT, commercial studies give more attention to service-oriented MADM and reputation, yet none adopt a probabilistic or Bayesian computation. Second, within the domain, application-layer schemes typically rank services or providers, whereas network-layer schemes combine weighted device and data scores with an optimizer. Third, in commercial IoT, event-driven trust updates are a common approach by 43% (Wang et al., 2025a; Sun et al., 2024; Latif et al., 2022; Chen et al., 2023; Yu et al., 2023; Guo et al., 2022b). In these frameworks, trust scores are revised after service collaboration, provider interaction, or QoS change. Time-driven mechanisms are also slightly more frequent in commercial IoT by 36% particularly in schemes that require continuous monitoring of device behavior (Ahmad et al., 2025; Rathee et al., 2025; Tuncel and Oztoprak, 2025; Sharma et al., 2024; Salim et al., 2022). Thirdly, frameworks we investigated that fall into industrial IoT domain are listed in Table 7. These frameworks mainly aim to preserve safe and continuous cyber-physical operation in factory, and sensor-network environments. Although the studies target different entities, they collectively seek to ensure that only trustworthy devices, data, edge services, and communication paths participate in industrial monitoring and control. Device trust is the dominant target in all three domains, but industrial IoT shows the strongest concentration on standalone device trust by 70% in all layers. (Wang et al., 2025c; Cheng et al., 2024; Yu et al., 2024; Li et al., 2023a; Kumar and Sharma, 2025; Wang et al., 2024; Wang et al., 2020) assess device trustworthiness, in addition to that, (Zhang et al., 2023) extends this by including data trustworthiness and (Feng et al., 2023) by integrating digital twins and blockchain, where trust scores guide autonomous service and server selection to support self-healing Edge-AI IIoT. The only research focus solely on data trust is (Ratnayake et al., 2024) where it addresses a core limitation of blockchain-enabled IoT: blockchain ensures tamper-resistant storage, on the other hand, it does not guarantee that uploaded sensor data is accurate or trustworthy. The authors therefore aim to evaluate data trust at the point of submission by combining blockchain-based past device reputation with real-time SVM-assisted data classification at the edge, and validator-driven ML ensemble verification for uncertain records. Although this context dominated by device trust, FDI remains the most frequently addressed attack (Ratnayake et al., 2024; Wang et al., 2025c; Feng et al., 2023; Zhang et al., 2023; Wang et al., 2024). It is because compromised industrial devices are seen as the main source of corrupted process data. Data quality and service-output validity are used as an additional evidences for device trustworthiness (Wang et al., 2025c; Feng et al., 2023; Wang et al., 2024). Only (Ratnayake et al., 2024) accept data trust in its evaluation scope, whereas the remaining device-trust studies mainly address reputation (Cheng et al., 2024; Yu et al., 2024), insider (Wang et al., 2020; Li et al., 2023a), collusion (Yu et al., 2024; Li et al., 2023a), or network attacks (Kumar and Sharma, 2025). (Yu et al., 2024) jointly models device interaction and communication behavior to broaden attack coverage, but it still treats the edge broker as implicitly trusted; the authors therefore suggest future direction of a trusted execution environment at the edge device. Computationally, industrial IoT trust is less a single modeling tradition than a set of mechanisms aligned with process integrity. Weighted feedback remains the most common formulation, aggregating terminal and device interaction evidence at the edge (Cheng et al., 2024; Yu et al., 2024). Commercial IoT uses similar aggregation, but typically to rank services or allocate resources through QoS and MCDA criteria (Sun et al., 2024; Latif et al., 2022). Industrial schemes instead filter MEC feedback and process data, treating trust as a gate on operational evidence rather than as a service-selection score. In learning-based models, Consumer IoT frameworks more often combines FL with fuzzy inference, generative sanitization, or probabilistic predictors for home and healthcare settings (Rehman et al., 2025; Le and Shetty, 2022). In industrial IoT, by contrast, it is used mainly for anomaly detection and rejection of corrupted sensor readings (Ratnayake et al., 2024; Li et al., 2023a). Blockchain appears in all three domains, yet its role differs. In industrial deployments, it supports cross-domain federation, digital-twin validation, and storage-consensus trust (Feng et al., 2023; Zhang et al., 2023), rather than the reputation archiving typical of consumer IoT (Zhang et al., 2021) or the service-market logging typical of commercial IoT (Liang et al., 2024). Trust updates in industrial IoT are mainly event-driven or hybrid (40% each). Scores change when data are submitted, interactions occur, or consensus proofs complete. Effective maintenance is important not to leave old trust score in the loop. Table 8. Comparative Taxonomy of Trust Management in Infrastructure IoT Research Scope Trust Design Evaluation & Security Ref. (Year) Evaluation Layer Trust Target Computation Model Trust Inputs Update Attacks Mitigated Validation Code? (Din et al., 2025) (2025) Application Device FL, GenAI Vehicle behavior, communication patterns, telemetry, recommendations, data credibility E Insider Simulation on CityPulse dataset, real-world urban traffic No (Xiang et al., 2025a) (2025) Application Data Weighted sum (direct+recommendation trust), DL anomaly detection Interaction satisfaction-success/failure, recommendations, data correctness E NC, FDI, OOA, BMA, impersonation, replay, MitM, ESL Trust-convergence simulation, formal RoR analysis No (Ismail et al., 2024) (2024) Application Device ML-based Direct T Insider, DoS Simulation on WSN-DS dataset No (Li et al., 2024b) (2024) Application Device Bayesian probabilistic trust model based on Beta distribution Hybrid E BMA, OOA, SFA, SA Simulation Available upon request (Chen et al., 2024) (2024) Application Device & Data UAV ground-truth accuracy, EWMA decay, Gaussian-based data rectification Reported data, per-timestep/historical accuracy T FDI, DP, collusion Experiments on 3 synthetic MCS datasets Yes (Li et al., 2023b) (2023) Application Device Challenge-based Hybrid E Betrayal, SOOA, PMFA Simulation, real network No (Yang et al., 2023) (2023) Application Device & Service Dirichlet reputation, time decay, punishment-revocation, direct/indirect trust Service ratings, rating freshness, interaction success/failure, reputation E False messages, BMA, OOA Simulation on real-world Chongqing taxi GPS dataset No (Wang et al., 2025b) (2025) Application & Physical Device & Data Weighted multi-dimensional trust with TOPSIS Direct T Malware/virus propagation, deceptive node attacks, DoS Simulation on MATLAB, OMNeT++ No E = Event-driven Update, T = Time-driven Update, E & T = Hybrid, - = Not Reported Lastly, research in infrastructure IoT trust are given in Table 8 and Table 9. This domain is the most device-centric domain in the reviewed set, with device trust appearing in 95%. However, unlike Industrial IoT, infrastructure studies more often combine multiple trust scope. Assessing device and data together 26%, reflects the need to validate both participating entities and mobility-generated observations in large-scale deployments. The evaluation focus also shifts downward to the network layer 53% of studies, where trust supports secure routing, relay selection, clustering, virtual-network embedding, and grid communication (Li et al., 2024b; Kaur and Kakkar, 2024; Ahmed et al., 2022; Mohanta et al., 2025; Han et al., 2024). (Ahmed et al., 2022) introduces link-history-based trust penalization as a load-balancing mechanism which is useful for routing/forwarding trust and edge-security sections. In addition to routing decisions, some frameworks further integrate trust with network resource management. For example, (Ahmed et al., 2022) incorporates link-history-based trust into load-balancing decisions, demonstrating that trust can also mitigate congestion and denial-of-service conditions by avoiding overloaded forwarding paths. Table 9. Continue Tab. 6 Research Scope Trust Design Evaluation & Security Ref. (Year) Evaluation Layer Trust Target Computation Model Trust Inputs Update Attacks Mitigated Validation Code? (Bounaira et al., 2025) (2025) Network Device Game theory Interaction history, reputation, location, resources E & T Malicious nodes interference, selfish behavior Simulation, hedonic coalition, Stackelberg games Yes (Mohanta et al., 2025) (2025) Network Device Probabilistic Digital signatures, ID credentials, energy usage data, timestamps E & T Single point of failure, unauthorized access, data tampering, privacy leaks Ethereum testbed No (Kaur and Kakkar, 2024) (2024) Network Device Weighted sum, forgetting factor, reward/penalty Packet-forwarding ratio, recommendations E Malicious nodes (false reports, misbehaving CH) Simulation on Python, Freeway mobility No (Li et al., 2024a) (2024) Network Device Interaction success/failure Historical interaction success/failure, real-time task-execution outcomes, node attributes E & T Malicious edge nodes, network/data/migration disruption, errors Simulation on Java, NetLogo No (Zhang et al., 2024c) (2024) Network Device Weighted sum, DRL for trust-constrained VNE Security level, node activity, communication status, VNR participation E Malicious host node, data tampering/leakage Simulation on NetworkX No (Zhang et al., 2024b) (2024) Network Device & Service Subjective logic, discounting/consensus, DQN energy prediction Ownership, honesty, social relationships, latency, PDR E & T Collusion, GMA, OOA, malicious recommendations Simulation on MATLAB, vs TwI-FTM, TTLA, MTTM No (Ahmed et al., 2022) (2022) Network Device Beta distribution, weighted direct+cumulative trust, recommendation aggregation Communication behavior, energy, data similarity, link history, recommendations E & T DoS, energy-based node compromise Simulation on MATLAB, vs BLTM No (Dehalwar et al., 2022) (2022) Network Device & Data Blockchain authentication, Merkle-tree/SHA-256 verification Identity credentials, signatures, block hashes, consensus votes, timestamps E Identity theft, masquerading, SA, FDI PoC with synthetic data No (Haseeb et al., 2022) (2022) Network Device & Data Weighted direct trust Link quality, delay, energy, relay trust, distance, mobility T FDI, malicious node attacks, confidentiality/auth threats Simulation on NS-3, vs fuzzy-IoT, CPSLP No (Qureshi and Shandilya, 2022) (2022) Network Device Weighted hybrid trust, differential evolution fitness Packet forwarding, availability, residual energy, connectivity, queue congestion T BHA Simulation on NS-2, vs LEACH, TMS, eeTMFO/GA No (Han et al., 2024) (2024) Network & Physical Device & Data RL Q-learning self-trust, signature-based backtracking Energy, activity, interaction frequency-success rate, data reliability, signatures E & T NC, data tampering, SFA Simulation No E = Event-driven Update, T = Time-driven Update, E & T = Hybrid, - = Not Reported Trust inputs are strongly mobility and resource-aware, using packet-forwarding behavior, interaction history, energy, link reliability, and location evidence. Computationally, infrastructure frameworks frequently couple trust with optimization or game-theoretic decision-making (Li et al., 2024a; Qureshi and Shandilya, 2022; Bounaira et al., 2025; Zhang et al., 2024c). FDI remains the most common attack by 37%, but reputation and forwarding attacks are also visible in vehicular and routing frameworks (Xiang et al., 2025a; Li et al., 2024b; Yang et al., 2023; Qureshi and Shandilya, 2022). Overall, the shared goal of infrastructure IoT trust management is to preserve dependable large-scale operation under mobility, multi-stakeholder governance, and critical-service availability constraints. In summary, Table 10 compares four application domains into a side-by-side comparison of how the surveyed frameworks differ in edge placement, evaluation layer, trust target, trust evidence acquisition, computation method, update pattern, and reproducibility aspects. For the edge utilization, studies in commercial and industrial IoT most often place trust computation at MEC brokers, service gateways, or Edge-AI nodes. Because service ranking and process-data filtering are latency-sensitive. Studies in consumer IoT have shifted toward federated and device-local training to keep household and health evidence off a central server. Infrastructure frameworks, despite our edge-enabled scope, more often evaluate trust on the forwarding path itself; UAV, RSU, or load-balancing edges appear, but many routing schemes remain node-local. Trust targets follow the operational object of each domain. Device trust dominates everywhere, yet commercial IoT is the only domain in which service trust is the most common design goal (36% versus 17%, 10%, and 11%). Data trust is rarely standalone: it is evaluated together with device trust in consumer IoT, used as a process-integrity check in industrial IoT, and paired with mobility observations in 26% of infrastructure IoT studies. In trust evidence acquisition, consumer and commercial IoT research typically take into consideration direct observations with recommendations, industrial IoT schemes weight interaction feedback at the edge whereas infrastructure IoT schemes rely more on direct network observables such as packet delivery, energy, and link quality. Adaptive computation, meaning models that revise weights or thresholds from observed behavior rather than from a fixed formula, reaches about half of consumer and commercial IoT studies. This includes federated learning, DRL offloading, fuzzy inference based works. But it is less common in industrial (40%) and infrastructure IoT (32%), where weighted, probabilistic, and game-theoretic rules still critical choice. Event-driven or hybrid updates are the majority pattern in all four domains; purely periodic updates remain a minority. This survey reveals that only three of 55 studies release public code, and seven more offer it upon request. Therefore, cross-paper comparison still depends on incompatible simulators and author-built traces. Table 10. Cross-Domain Comparison of Characteristic Trust-Design Choices (N=55N=55) Design Item Consumer IoT (N=12N=12) Commercial IoT (N=14N=14) Industrial IoT (N=10N=10) Infrastructure IoT (N=19N=19) Edge Utilization Federated or local training, cloud-edge reputation. Highest fog/MEC placement, service brokers and offloading. Edge filters feedback, Edge-AI and digital twins. Path-level computation, UAV/RSU edges, often node-local. Evaluation Layer App 33%, cross-layer 33%, net 25%. Net 50% and app 43%, service/device split. App 60%, gates industrial control. Net 53% for routing and clustering, app 37%. Trust Target Device 92%, data 42%, service 17%. Device 71%, service 36%, data 29%. Standalone device 70%, service 10%. Device 95%, data 26%. Trust Evidence Direct & recommendation, link and context signals. Hybrid and QoS/SLO attributes and delay. Edge-aggregated feedback, data quality as device proof. Mobility and resource metrics, PDR, energy, location. Adaptive Computation 50% learning-based, weighted aggregation. 50% with DRL, FL, MCDA alternatives. 40% for anomaly detection and sensor rejection. Lowest at 32%, defaults to Beta/Dirichlet and game theory. Trust Update Hybrid 42%, event or time 25% each. Event 43%, time 36%, hybrid 21%. Event and hybrid 40% each, transaction-driven. Event 42%, hybrid 32%, mobility-driven handover. Code Availability None public, 2 upon request, simulation-heavy. 1 public, 1 upon request, simulator-heavy. None public, 3 upon request, some prototypes 2 public, 1 upon request, simulation and Ethereum tests. 4.1. Roles of Trust in IoT Operations We investigate many state-of-art studies focusing how trust values obtained and computed. It is equally important to examine how these trust scores are actually utilized in operational decision-making. Instead of just saying a device, data or service is trusted, we need to map that trust to specific actions. This work surveys representative trust-supported IoT operations, summarized in Table 11 and illustrated in Figure 7. Table 11. Taxonomy of Trust-Supported Decisions in IoT Operations Category Trust-Guided Question Operational Action References Service Selection Which provider should be invoked? Choose service instance or supplier (Wang et al., 2025a; Huang et al., 2025; Bampatsikos et al., 2025; Bangui et al., 2025; Tsekenis et al., 2025; Yu et al., 2024; Sun et al., 2024; Liang et al., 2024; Souri et al., 2024; Feng et al., 2023; Yu et al., 2023; Wang et al., 2023b; Guo et al., 2022b; Qureshi and Shandilya, 2022; Lu et al., 2025) Secure Routing & Forwarding Who should relay packets next? Select next hop or path (Hassan et al., 2024; Sharma et al., 2025; Deng et al., 2024; Farooq et al., 2022; Airehrour et al., 2019; Gheisari et al., 2025; Wang et al., 2024; Khan et al., 2024; Zhang et al., 2024b; Kaur and Kakkar, 2024; Ahmed et al., 2022; Reddy et al., 2025; Han et al., 2024; Sharma et al., 2024; Haseeb et al., 2022) Access Control Should the entity be allowed in? Grant, deny, or revoke access (Zhang et al., 2024b; Ahmad Awan et al., 2025; Ahmed et al., 2025; Padmavathi and Saminathan, 2025; Xiang et al., 2025a; Wang et al., 2024; Ali et al., 2024; Egala et al., 2023; Ajao and Apeh, 2023; Dehalwar et al., 2022; Wazid et al., 2022; Latif et al., 2022; Fang et al., 2020) Intrusion Detection Is the entity malicious? Detect, classify, or isolate misbehavior (Anwer et al., 2025; Rehman et al., 2025; Han et al., 2024; Rathee et al., 2025; Ali et al., 2024; Li et al., 2023a; Moudoud et al., 2022; Verma and Chandra, 2023; Li et al., 2020; Wang et al., 2021; Alsheakh and Bhattacharjee, 2020) Computation Offloading Where should the task execute? Offload to local, edge, or cloud node (Bounaira et al., 2025; Zhang et al., 2024c; Li et al., 2024a; Liang et al., 2024; Chen et al., 2023; Faraji et al., 2024; Souri et al., 2024; Zhu et al., 2024; Ahmed et al., 2022) Secure Clustering & Grouping Which nodes form a stable team? Form clusters; elect cluster head (Messina et al., 2025b; Xiang et al., 2025b; Tuncel and Oztoprak, 2025; Kuppusamy and Murugesan, 2024; Kaur and Kakkar, 2024; Fortino et al., 2023; Patnaik et al., 2023; Qureshi and Shandilya, 2022; Salim et al., 2022) Data Aggregation Which reports should be fused? Accept, reject, or weight sensor data (Wang et al., 2025b; Khan et al., 2025b; Sharma et al., 2024; Moudoud et al., 2022; Guo et al., 2022a; Salim et al., 2022; Al-Maslamani et al., 2022; Zhang et al., 2021; Ratnayake et al., 2024) Privacy Preservation What must stay hidden? Anonymize or restrict sensitive disclosure (Li et al., 2024b; Zehra et al., 2024; Osamy et al., 2025; Gheisari et al., 2025; Gnanajeyaraman et al., 2023; Le and Shetty, 2022; Ratnayake et al., 2024) Trusted Data Sharing To whom should data be sent? Select trusted relay, peer, or cloud endpoint (Khan et al., 2025a; Wang et al., 2025c; Kumar and Sharma, 2025; Gnanajeyaraman et al., 2023; El Majdoubi et al., 2020; Kuppusamy and Murugesan, 2024) Bar chart showing the distribution of operational decisions supported by trust management in the surveyed literature. Figure 7. Distribution of Operational Decisions Supported by Trust Management in the Surveyed LiteratureBar chart showing the distribution of operational decisions supported by trust management in the surveyed literature. The obtained results show that trust is most frequently integrated into routing and forwarding and as well service selection decisions, indicating that trust management is primarily used to regulate which entities an IoT node should interact with. Access control and intrusion detection are also prominent, extending this role from selecting communication partners to deciding whether an entity should be admitted or considered malicious. Together, these categories show that current frameworks use trust as a preventive or selective mechanism for controlling interactions with potentially unreliable IoT entities. In contrast, trust is less frequently incorporated into computation offloading, secure clustering, and data aggregation. These applications require trust to be combined with additional operational objectives such as resource allocation, energy efficiency, data quality, or group formation, suggesting that trust is less often treated as the primary decision criterion when multiple system-level objectives must be optimized. Data-oriented decisions are particularly limited: trusted data sharing, data aggregation and privacy preservation receive less attention than communication and device-related decisions. This shows that trust is more often used to evaluate devices than the data they provide or share. 4.2. Attack Mitigation in State-of-the-Art Frameworks We examine how state-of-the-art trust management frameworks use trust to mitigate attacks. It is equally important to identify which attacks these frameworks address and how broadly their security mechanisms are evaluated. Table 12 summarizes the attacks considered by the surveyed frameworks. FDI is the most frequently addressed attack, followed by BMA, GMA, SA, and OOA. Within the literature analyzed by (Konsta et al., 2022), BMA, GMA, and SPA emerge as the most frequently addressed trust-related attacks, while OOA and OSA receive less attention. These attacks affect the evidence used to assess entities or directly alter their perceived behavior. DP is also repeatedly considered particularly in learning-based and data-driven trust mechanisms. Table 12. Mapping of Studies to Mitigated Trust-Related Attacks Study NC SDA SA FDI JA BMA GMA SPA DP OOA SFA BHA GHA OSA DA WA (Ahmad et al., 2025) – ✓ ✓ – – – – – – ✓ – – – – ✓ – (Ahmed et al., 2025) – – ✓ – – – – – – – – – – – ✓ – (Anwer et al., 2025) – – ✓ ✓ – – – – – – ✓ – – ✓ ✓ – (Chen et al., 2023) – – – – ✓ – – – – – – – – – – – (Chen et al., 2024) – – – ✓ – – – – ✓ – – – – – – – (Han et al., 2024) ✓ – – ✓ – – – – – – ✓ – – – – – (Islam et al., 2025) – – ✓ ✓ – – – – – – – – – ✓ – – (Ismail et al., 2024) – – ✓ – – – – – – – ✓ – – – – – (Khoshvaght et al., 2025) – – – ✓ – ✓ ✓ ✓ – – – – – – ✓ – (Kumar and Sharma, 2025) ✓ – ✓ – – – – – – – – – – – ✓ – (Le and Shetty, 2022) – – ✓ ✓ – – – – ✓ – – – – – – – (Li et al., 2023a) – – ✓ – – – – – – ✓ – – – – – – (Mahmood et al., 2025b) – – – – – ✓ ✓ – – – – – – – – – (Moeinaddini et al., 2025) – ✓ – – – ✓ ✓ ✓ – ✓ – – – ✓ ✓ – (Patnaik et al., 2023) – – ✓ – – ✓ ✓ – – – – – – – – ✓ (Ranathunga et al., 2021) – – – – ✓ – – – – – – ✓ – – – – (Rehman et al., 2025) ✓ – – – – ✓ ✓ – – ✓ – – – – ✓ – (Sharma et al., 2024) – – – ✓ – ✓ – – – – – – ✓ – – – (Verma and Chandra, 2023) – – ✓ – – – – ✓ – – – – – – – ✓ (Wang et al., 2021) – – – ✓ – ✓ – – – – ✓ – – – – – (Xiang et al., 2025a) ✓ – – ✓ – – – – – – – – – – – – (Zehra et al., 2024) – – ✓ ✓ ✓ – – – – – – – – – – – (Moudoud et al., 2022; Bampatsikos et al., 2025) – – – ✓ – ✓ ✓ ✓ ✓ – – – – – – – (Qureshi and Shandilya, 2022; Salim et al., 2022) – – – – – – – – – – – ✓ – – – – (Zhang et al., 2024b; Fortino et al., 2023) – – – – – ✓ ✓ ✓ – ✓ – – – – – – (Yang et al., 2025; Dehalwar et al., 2022) – – ✓ ✓ – – – – – – – – – – – – (Yu et al., 2024; Li et al., 2020) – – – – – ✓ ✓ – – ✓ – – – – – – (Yu et al., 2023; Fang et al., 2020) – – – ✓ – – – – – ✓ – – – – – – (Yang et al., 2023; Zhang et al., 2021) – – – ✓ – ✓ – – – ✓ – – – – – – (Ajao and Apeh, 2023; Singh and Park, 2023; Sylla et al., 2021) ✓ – ✓ ✓ – – – – – – – – – – – – (Khan et al., 2025b; Al-Maslamani et al., 2022; Wang et al., 2023b) – – – – – – – – ✓ – – – – – – – (Guo et al., 2022a; Latif et al., 2022; Wang et al., 2020; El Majdoubi et al., 2020; Wang et al., 2025c; Haseeb et al., 2022; Huang et al., 2025) – – – ✓ – – – – – – – – – – – – The remaining attacks NC, JA, SFA, BHA, GHA, OSA, DA, and WA appear in only a small number of studies. This does not directly indicate that they are less relevant to trust management. But, for example, NC can change the trust status of an entity after compromise. JA can affect the evidence available for trust assessment. SFA, BHA, and GHA can directly alter the observed forwarding behavior of an entity. Similarly, OSA, DA, and WA can manipulate service behavior or the historical evidence associated with an entity. Thus, the reviewed attacks can all be interpreted as threats to the trust assessment, trust evidence, or trust-driven decision process. The main difference is where they affect this process and which trust evidence they manipulate. Therefore, we studied this issue in Table 4. A notable gap is that relatively few frameworks evaluate several of these attack types together. For example, (Moeinaddini et al., 2025) addresses SDA, BMA, GMA, SPA, OOA, OSA, and DA, while (Moudoud et al., 2022; Bampatsikos et al., 2025) jointly consider FDI, BMA, GMA, SPA, and DP. However, this shows that recent frameworks increasingly evaluate trust under multiple forms of adversarial behavior rather than a single attack model. 5. The Road Ahead: Building a Trustworthy IoT Ecosystem As IoT devices increasingly make autonomous, localized decisions at the edge, trust management can no longer rely on simple scalar values. It must consistently evaluate devices, data, and services across the edge-cloud continuum while withstanding mobility, resource constraints, and trust-related attacks. Although emerging paradigms like edge AI, federated learning, blockchain, and 6G enable distributed trust, they also introduce novel complexities. 5.1. Research Challenges In order to critically evaluate the current state of the art and map the trajectory of trust management research, we review the recurring limitations in trust-model design, security, deployment, and ecosystem readiness, as summarized in Figure 8. Frequency distribution of research challenges in surveyed literature. Figure 8. Frequency Distribution of Specific Research Challenges Identified in the Surveyed Literature (N=55)Frequency distribution of research challenges in surveyed literature. 5.1.1. Architectural and Deployment Challenges Maintaining consistent trust across distributed edge, fog, and cloud infrastructures (C1) has gained increasing attention over the years. It emerges as the most cited architectural challenge in the state-of-art studies. This challenge has grown from a minority concern in 2020–2021 to a central focus by 2025 as edge AI, digital twins, and cloud–edge–terminal architectures become the dominant deployment paradigm (Khan et al., 2025b; D’aniello and Fotia, 2025; Konsta et al., 2022; Ranathunga et al., 2021). The challenge here is not just the physical placement of computing, but also ensuring the consistency of trust state across these different tiers. Closely related to this challenge, the degree of centralization that trust management should adopt is another important research challenge (C2). This challenge arises because fully distributed peer-based trust mechanisms converge slowly and rely on sparse local observations, whereas centralized authorities provide globally consistent trust decisions at the cost of higher latency and single points of failure (Gnanajeyaraman et al., 2023). Beyond maintaining trust consistency in an appropriate trust management architecture, trust mechanisms must also adapt to constantly changing network topologies. Supporting mobility and dynamic topologies (C3) becomes essential especially in between vehicles, or UAV. Because in mobile networks, trust evidence can quickly become outdated due to handovers, intermittent connectivity, and network partitions. Therefore, maintaining accurate and timely trust assessment is challenging (Zhang et al., 2024a; Farooq et al., 2022; Reddy et al., 2025). Trust management in IoT must also remain efficient at large scale. Ensuring scalability (C4) requires controlling the communication, computation, and storage overhead introduced by trust management as IoT deployments grow. The first thing that comes to mind when scalability is addressed is generally the size of the network. However, it is important to emphasize that scalability issues are also due to various types of traffic, topology changes due to mobility, and the need to perform trust evaluation, routing, and quality of service simultaneously (Reddy et al., 2025; Xiong et al., 2023). In the research by (Konsta et al., 2022), it is noted that IoT nodes may accumulate large volumes of trust-related information, yet storage complexity analysis is almost absent. One frequently proposed response to the scalability limitations of trust management systems is the use of blockchain-based infrastructures. Evaluating blockchain trade-offs (C5) therefore becomes an important challenge on its own. While blockchain can provide tamper-evident trust records and decentralized consensus mechanisms (Egala et al., 2023; Jmal et al., 2025; Konsta et al., 2022; Feng et al., 2023; Ismail et al., 2024), its practical deployment introduces additional overhead through ledger growth, consensus latency, and on-chain trust-state storage. These costs are often difficult to accommodate on constrained IoT devices, and existing hybrid solutions rarely provide comprehensive cost models spanning computation, communication, and storage resources. Whenever trust information is shared beyond a single device, the network must carry additional control traffic, including reputation broadcasts, certificate or token exchanges, blockchain transactions, and federated or edge-AI model updates. This overhead consumes the same constrained LPWAN and low-power mesh links used for application data, increasing latency, packet loss, and energy consumption even when the underlying trust algorithm is computationally lightweight (Wang et al., 2023b; Wazid et al., 2022; Muzammal et al., 2020). Although only six studies among surveyed research explicitly identify communication and trust-update overhead (C6) as an open research challenge. Because in state-of-art research many suggestions assume that trust is spread on a regular basis, but they do not report how often messages are sent, how big updates are, or how this affects network overall. Maintaining trust is not cost-free. Update propagation add communication and energy overhead to IoT nodes (Yang et al., 2023). Nevertheless, current literature often prioritizes the initial trust computation over the development of sustainable, ongoing maintenance mechanisms. 5.1.2. Trust Model Design and Algorithmic Robustness Beyond architectural considerations, trust management fundamentally depends on the ability to derive reliable trust scores from incomplete and changing evidence. Designing robust trust models (C7) therefore remains a major research challenge. Trust models must cope with cold-start conditions, sparse interaction histories (Guo et al., 2022a), dynamic trust updates, and indirect recommendations while adapting to rapidly changing IoT topologies, workloads, and device roles (Messina et al., 2025b; Wang et al., 2023a). Trust models often ignore evaluating transmitted information. The difference between device misbehavior and data untrustworthiness is frequently ignored by many research (Kumar and Sharma, 2025). (Tadj et al., 2023; Xiang et al., 2025a; Konsta et al., 2022) highlight the data integrity problem (C8). For instance, an IoT device can execute routing protocols perfectly but still send noisy crowdsourced labels, or falsify sensor readings. These issues require data-driven trust assessment. Most schemes still incorrectly infer data quality based solely on a device’s network-layer behavior, making systems vulnerable to false data from trusted nodes. Moreover, trust management should also account for the context in which trust is established. Supporting multi-dimensional and context-aware trust (C9) recognizes that a single trust score is often insufficient for IoT systems. A device may be trustworthy for one task but not for another. Likewise, changes in location, or operating conditions should not always be interpreted as malicious behavior. Although fuzzy and policy-based trust models consider these factors, there is still no common framework for representing and managing context-aware trust across IoT environments (Rehman et al., 2025; Sylla et al., 2021). Integrating AI/ML and FL (C10) introduces a new set of challenges. AI-based trust models can better detect malicious behavior and adapt to changing network conditions using techniques such as ensemble learning, graph neural networks, and deep reinforcement learning (Chen et al., 2023; Al-Maslamani et al., 2022; Le and Shetty, 2022). However, these approaches are also vulnerable to poisoning attacks, adversarial inputs, and limited model interpretability. Defining trust-aware response policies (C11) extends beyond simply identifying malicious participants. Most existing trust schemes react by excluding or isolating nodes once their trust falls below a threshold. However, this approach is often unsuitable for sparse, mobile, or safety-critical IoT networks, where removing a participant may reduce network connectivity or service availability. More flexible response strategies, such as graduated sanctions, temporary quarantine with recovery, and distinguishing unreliable from intentionally malicious behavior, remain largely unexplored (Wang et al., 2022b; Faraji et al., 2024; Ali et al., 2024). 5.1.3. Adversarial Threats, Privacy, and Trust-Aware Security Trust management should also protect the trust process from attacks. Defending against trust-targeted attacks (C12) requires securing different parts of the trust ecosystem. Attackers may target the trust pipeline by manipulating how trust evidence is collected, shared, updated, or aggregated, leading to incorrect trust decisions even when individual devices are not compromised. Although many studies discuss these attacks, most evaluate only a single attack type (Moeinaddini et al., 2025; Reddy et al., 2025). While C12 focuses on protecting the trust process from attacks, trust management should also protect the sensitive information used to establish trust. Preserving privacy (C13) is challenging because trust evaluation relies on sensitive information and may subject to privacy regulations (Osamy et al., 2025; Gheisari et al., 2025). Techniques such as FL and differential privacy reduce data exposure, but they also limit the information available for trust assessment and remain vulnerable to poisoning attacks (Feng et al., 2023). As a result, balancing privacy and trust accuracy remains an open research problem. The challenge in authentication and access-control integration (C14) in trust management frameworks defines a third barrier at the security boundary: cryptographic authentication does not detect compromised insiders, while behavioral trust alone lacks strong identity binding (Wazid et al., 2022; Mahmood et al., 2025b; Muzammal et al., 2020; Hossain et al., 2024). 5.1.4. Ecosystem Readiness and Practical Constraints Although resource constraints (C16), standardization and governance (C17), industrial deployment (C18), heterogeneity (C19), large-scale smart city ecosystems (C20), and Social IoT environments (C21) have long been recognized as fundamental IoT challenges, they continue to be highlighted in recent trust management research. These are not new challenges to the IoT community; however, recent studies continue to identify them as major barriers to deploying practical trust management systems. In addition to these ecosystem challenges, trust management still lacks reliable evaluation methods. Establishing reproducible evaluation methodologies (C15) remains a persistent challenge, although only a few studies identify it explicitly. Previous surveys report inconsistent benchmarks, incompatible simulators, limited public code, and evaluation settings that differ in topology, attack models, and performance metrics, making fair comparison difficult (De Keersmaeker et al., 2023; Kamble et al., 2024; Chandrasekaran and Rajasekaran, 2024; Akli and Chougdali, 2023). Public trust datasets are also limited, often lacking interaction history or recommendation data. As a result, many studies prefer relying on synthetic data or custom datasets (Wang et al., 2021; Chen et al., 2024; Tadj et al., 2023). Figure 9 groups the 55 surveyed papers into four broad area over 2020-2025. The trend suggests a shift from designing trust models toward deploying and securing them in realistic IoT environments. Trust-model challenges remain dominant, while architectural and adversarial challenges increase notably by 2025. This reflects growing attention to edge AI, distributed trust computation, model poisoning, and other attacks that can manipulate trust evidence or learning processes. In contrast, ecosystem-related challenges show limited growth. Resource constraints are frequently considered, but interoperability, standardization, evaluation, and cross-domain deployment remain less explored. These results indicate that the field is moving beyond trust-score design, while practical questions about how trust mechanisms operate across heterogeneous IoT environments and remain reliable under evolving attacks are still insufficiently addressed. Research Challenges Trends by Years (a) Research Challenges Trends by Years Research Challenges Trends by Broad Areas (b) Research Challenges Trends by Broad Areas Figure 9. Research Challenge Trends in the Reviewed Literature by (a) Yearly Distribution and (b) Broad Areas 5.2. Future Directions After the challenges discussed in Section 5.1 what remains open is how the community should tackle them in specific IoT trust designs. Through this perspective, future directions are given below to provide broad technology trends. 5.2.1. Privacy-Preserving Trust Computation Trust calculation requires behavioral and contextual information that may also be valuable to attackers. A central direction is privacy-preserving trust computation, where nodes share abstract trust information instead of raw evidence (Takemura et al., 2024). Semantic trust can reduce direct data exposure by expressing which device capability, data category, or service role is trusted under a given policy; however, semantic representations may still allow sensitive information to be inferred. FL and cryptographic mechanisms could complement semantic trust to further protect the underlying evidence while keeping identifiers, location traces, and rating histories local. These kind of mechanisms are specifically valuable in applications in consumer IoT domain. 5.2.2. Sustainable Trust Management in 6G 6G introduces new requirements for both sustainability and ubiquitous connectivity. Future trust mechanisms should reduce communication, computation, and energy costs, especially in infrastructure IoT. Non Terrestrial Networks, including satellites, UAVs, HAPs, and LEOs can support trust dissemination and computation, but designs must account for mobility, intermittent connectivity, propagation delay, and overhead (Mahmood et al., 2025a). Validation must report message rate, update size, latency, and energy per trust score computation and dissemination. 5.2.3. Agent-to-Agent Trust IoT systems are moving from device-centric networks toward autonomous agents, including AI-integrated end devices. A future direction is agent-to-agent trust (Sheng et al., 2024), where agents evaluate one another’s behavior, authority, and reliability from operational evidence rather than identity alone. An emerging extension is to use agents themselves as trust parties: regional trust checkers and managers at the edge could issue short-lived claims, admit or revoke nearby devices, and continuously re-verify peers as their behavior or goals change. Commercial IoT is a favorable choice for this approach, where trust can directly support decisions such as which agent may provide a service, accept a task, or access a resource. However, introducing trust managers also creates a new type of vulnerability: a compromised checker could manipulate trust claims and influence decisions within its region. Future work should therefore examine how such trust authorities can be secured and contained without disrupting the wider network. 5.2.4. Trust in Virtual, Immersive and Generative IoT Environments Future research should investigate how trust management changes when IoT systems increasingly rely on virtual models and AI-generated information. Digital twins can provide additional evidence for trust assessment by simulating device behavior, testing attack scenarios, and supporting trust bootstrapping for entities even with limited interaction history (Sasikumar et al., 2023). However, the trustworthiness of twin-generated evidence itself remains an open question. Similarly, generative AI can support trust prediction and evidence analysis while also introducing new risks through synthetic or manipulated sensor data, identities, and recommendations. Therefore, future trust mechanisms should distinguish between physical and generated evidence and assess their consistency and reliability before using them in trust decisions (Hasan et al., 2024). This is particularly relevant in large-scale Industrial and Infrastructure IoT, where virtual observations may influence routing, service selection, data aggregation, and other operational decisions. Hybrid physical-virtual testbeds could provide a suitable basis for evaluating these mechanisms under false-data and synthetic-evidence attacks. 6. Conclusion Trust management in edge-enabled IoT requires defining precisely who is trusted, for what operational decision, and against which specific threat. Because prior literature reviews have predominantly restricted their scope to device trust or isolated application domains, this survey adopts a multifaceted analysis to establish a unified perspective. We introduced a comprehensive taxonomy encompassing device, data, and service trust, mapped across a complete trust lifecycle from evidence acquisition to maintenance. Following PRISMA, we systematically analyzed 55 recent studies to evaluate their structural design dimensions across four key IoT domains (consumer, commercial, industrial, and infrastructure) and three architectural layers (application, network, physical). Our findings indicate that while device-centric frameworks continue to dominate, domain-specific priorities are evolving. Commercial IoT increasingly emphasizes service-level trust, whereas infrastructure deployments frequently integrate device and data trust to support high-mobility environments. In terms of mechanics, trust evidence is primarily derived from interaction histories, reputation metrics, and network observables. Computation models remain largely weighted or probabilistic, though they are increasingly augmented by machine learning and blockchain architectures, with updates handled predominantly through event-driven mechanisms. Consequently, while the research community has largely converged on how to compute trust values, a fundamental consensus regarding what specific target those scores should represent remains missing. Furthermore, the operational utilization of trust remains significantly narrower than its theoretical design space. Computed trust scores currently function primarily as gatekeepers for basic service selection, secure routing, and access control. Crucial system-level operations such as data aggregation, computation offloading, and privacy-preserving evaluations are rarely integrated, meaning many generated trust metrics are never fully leveraged to drive the decisions that justify their collection. The results for attack coverage reveal that False Data Injection (FDI) emerges as the most frequently addressed vulnerability, followed closely by Sybil attacks (SA), bad-mouthing (BMA), on-off (OOA), and good-mouthing (GMA) attacks. This hierarchy reflects the prevalence of device-oriented schemes, where anomalous data readings are typically penalized as node misbehavior rather than treated as distinct data-trust failures. Meanwhile, routing and service-layer exploits such as black-hole and selective forwarding attacks remain critically underexplored. To design edge-enabled IoT trust management systems into robust, deployable ecosystems, future research must prioritize: (i) maintaining clear, structural distinctions between device, data, and service trust under active attack; (i) benchmarking trust overheads against measured energy, latency, computation and communication costs; (i) engineering targeted defenses against decision-flow and resource-exhaustion exploits rather than focusing solely on evidence poisoning; (iv) adopting reproducible evaluation frameworks supported by public datasets and open source code; and (v) validating proposed designs within agent-based, 6G-supported and physically-virtually coupled environments. Acknowledgements This research was supported by Odine and OdineLabs as part of ImAgSLab Project under the 1773 ITU TTO Project No: 202500303, and by the ZHAW EELISA Sonderfinanzierung SePRIO project. References Aaqib et al. (2023) M. Aaqib, A. Ali, L. Chen, and O. Nibouche IoT trust and reputation: a survey and taxonomy. Journal of Cloud Computing 12 (1), p. 42. Cited by: §1.1, Table 1. Aaqib et al. (2024) M. Aaqib, A. Ali, L. Chen, and O. Nibouche Behavior-based interpretable trust management for iot systems. In 2024 35th Irish Signals and Systems Conference, ISSC 2024, H. Zheng, I. Cleland, A. Moore, H. Wang, D. Glass, J. Rafferty, R. Bond, and J. Wallace (Eds.), Irish Signals and Systems Conference. External Links: Document, ISSN 2688-1446, ISBN 979-8-3503-5299-3; 979-8-3503-5298-6 Cited by: §1. Ahmad Awan et al. (2025) K. Ahmad Awan, I. U. Din, A. Almogren, Z. Han, and M. Guizani TrustAware-gnn: graph-neural-network-based trust management for iot anomaly detection. IEEE Internet of Things Journal 12 (18), p. 37670–37681. External Links: Document, ISSN 2327-4662 Cited by: Table 11. Ahmad et al. (2025) I. Ahmad, S. Gimhana, I. Ahmad, and E. Harjula Adaptive trust architecture for secure iot communication in 6g. IEEE Networking Letters 7 (2), p. 113–116. External Links: Document Cited by: §2.2.4, Table 12, Table 6, §4, §4, §4. Ahmed et al. (2022) A. Ahmed, K. N. Qureshi, M. Anwar, F. Masud, J. Imtiaz, and G. Jeon Link-based penalized trust management scheme for preemptive measures to secure the edge-based internet of things networks. Wireless Networks 30 (5), p. 4237–4259. External Links: Document, ISSN 1022-0038 Cited by: §2.2.1, Table 11, Table 11, Table 9, §4. Ahmed et al. (2025) K. I. Ahmed, M. Tahir, S. L. Lau, M. H. Habaebi, A. Ahad, and A. Mughees Trust-aware authentication and authorization for iot: a federated machine learning approach. IEEE Internet of Things Journal 12 (8), p. 9889–9904. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 12, Table 5, §4, §4. Airehrour et al. (2019) D. Airehrour, J. A. Gutierrez, and S. K. Ray SecTrust-rpl: a secure trust-aware rpl routing protocol for internet of things. Future Generation Computer Systems 93, p. 860–876. Cited by: Table 11. Ajao and Apeh (2023) L. A. Ajao and S. T. Apeh Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning. Intelligent Systems with Applications 18, p. 200216. External Links: Document Cited by: Table 11, Table 12. Akli and Chougdali (2023) A. Akli and K. Chougdali A survey on machine learning for iot trust management. In 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Dubai, United Arab Emirates, p. 59–65. Cited by: §2.2.2, §2.2.3, §5.1.4. Al-Maslamani et al. (2022) N. Al-Maslamani, M. Abdallah, and B. S. Ciftler Secure federated learning for iot using drl-based trust mechanism. In 2022 International Wireless Communications and Mobile Computing, IWCMC, International Wireless Communications and Mobile Computing Conference, p. 1101–1106. External Links: Document, ISSN 2376-6492, ISBN 978-1-6654-6749-0 Cited by: Table 11, Table 12, §5.1.2. Alhandi et al. (2023) S. A. Alhandi, H. Kamaludin, and N. A. M. Alduais Trust evaluation model in iot environment: a comprehensive survey. Ieee Access 11, p. 11165–11182. Cited by: §1.1, Table 1, §1, §2.1, §2.2.1. Ali et al. (2024) H. Ali, A. N. Alvi, M. Alkhathami, D. Alsadie, and F. Alashaib TIHCS: trust-based improved qos for health care systems in smart cities. IEEE Access 12, p. 108755–108769. External Links: Document, ISSN 2169-3536 Cited by: §2.2.2, §2.2.3, Table 11, Table 11, Table 5, §4, §4, §5.1.2. AlMarshoud et al. (2024) M. AlMarshoud, M. Sabir Kiraz, and A. H. Al-Bayatti Security, privacy, and decentralized trust management in vanets: a review of current research and future directions. ACM Computing Surveys 56 (10), p. 1–39. Cited by: Table 1. Alsheakh and Bhattacharjee (2020) H. Alsheakh and S. Bhattacharjee Towards a unified trust framework for detecting iot device attacks in smart homes. In 2020 IEEE 17th International Conference on Mobile Ad Hoc and Smart Systems (MASS 2020), IEEE International Conference on Mobile Ad-hoc and Sensor Systems, p. 613–621. External Links: Document, ISSN 2155-6806, ISBN 978-1-7281-9866-8 Cited by: §2.2.2, Table 11, Table 5, §4, §4. Anwer et al. (2025) R. W. Anwer, M. Abrar, A. Salam, and F. Ullah TEAD: trust-enhanced anomaly detection framework for intrusion detection in iot-enabled wireless sensor networks (wsns). Wireless Networks 31 (6), p. 4179–4197. External Links: Document, ISSN 1022-0038 Cited by: Table 11, Table 12. Bampatsikos et al. (2025) M. Bampatsikos, I. Politis, T. Ioannidis, and C. Xenakis Trust score prediction and management in iot ecosystems using markov chains and madm techniques. IEEE Transactions on Consumer Electronics 71 (1), p. 862–882. External Links: Document, ISSN 0098-3063 Cited by: §1, §2.2.2, §4.2, Table 11, Table 12, Table 5, §4, §4. Bangui et al. (2025) H. Bangui, B. Buhnova, M. Ge, and S. Kriglstein Leveraging the internet of behaviors for mutual trust in digital ecosystems. In Companion Proceedings of the 2025 Conference on Intelligent User Interfaces, IUI 2025, p. 82–86. External Links: Document, ISBN 979-8-4007-1409-2 Cited by: §2.2.1, Table 11. Bounaira et al. (2025) S. Bounaira, A. Alioua, A. M. Vegni, and I. Shayea Trustworthy computation offloading in digital twin edge networks: a hierarchical game-based approach. Ad Hoc Networks 179. External Links: Document, ISSN 1570-8705 Cited by: §1, Table 11, Table 9, §4. Chaganti (2025) K. C. Chaganti A scalable, lightweight ai-driven security framework for iot ecosystems: optimization and game theory approaches. IEEE Access 13, p. 72235–72247. External Links: Document, ISSN 2169-3536 Cited by: §2. Chandrasekaran and Rajasekaran (2024) S. K. Chandrasekaran and V. A. Rajasekaran Trust evaluation model in iot environment: a review. Environment, Development and Sustainability, p. 1–32. Cited by: §1, §1, §2.1, §5.1.4. Chen et al. (2023) M. Chen, M. Yi, M. Huang, G. Huang, Y. Ren, and A. Liu A novel deep policy gradient action quantization for trusted collaborative computation in intelligent vehicle networks. Expert Systems with Applications 221, p. 119743. External Links: Document, ISSN 0957-4174 Cited by: §2.2.2, Table 11, Table 12, Table 6, §4, §4, §4, §5.1.2. Chen et al. (2024) Z. Chen, Z. Qu, N. Xiong, A. Liu, M. Dong, T. Wang, and S. Zhang UITDE: a uav-assisted intelligent true data evaluation method for ubiquitous iot systems in intelligent transportation of smart city. IEEE Transactions on Intelligent Transportation Systems 25 (8), p. 9597–9607. External Links: Document, ISSN 1524-9050 Cited by: §2.1, Table 12, Table 8, §5.1.4. Cheng et al. (2024) M. Cheng, W. Chen, W. Fang, J. Liu, T. Xu, and Z. Wang Feedback mechanism-based trust evaluation model for mobile edge computing in industrial iot. In Advanced Intelligent Computing Technology and Applications, PT VIII, ICIC 2024, D. Huang, W. Chen, and Y. Pan (Eds.), Lecture Notes in Computer Science, Vol. 14869, p. 461–469. External Links: Document, ISSN 0302-9743, ISBN 978-981-97-5602-5; 978-981-97-5603-2 Cited by: Table 7, §4, §4. De Keersmaeker et al. (2023) F. De Keersmaeker, Y. Cao, G. K. Ndonda, and R. Sadre A survey of public iot datasets for network security research. IEEE Communications Surveys & Tutorials 25 (3), p. 1808–1840. Cited by: §5.1.4. Dehalwar et al. (2022) V. Dehalwar, M. L. Kolhe, S. Deoli, and M. K. Jhariya Blockchain-based trust management and authentication of devices in smart grid. Cleaner Engineering and Technology 8, p. 100481. External Links: Document, ISSN 2666-7908 Cited by: Table 11, Table 12, Table 9. Deng et al. (2024) M. Deng, Y. Lyu, C. Yang, F. Xu, M. Ahmed, N. Yang, Z. Xu, and C. Ke Lightweight trust management scheme based on blockchain in resource-constrained intelligent iot systems. IEEE Internet of Things Journal 11 (15), p. 25706–25719. External Links: Document, ISSN 2327-4662 Cited by: §2, Table 11. Din et al. (2025) I. U. Din, A. Almogren, Z. Han, and M. Guizani Building reliable iot ecosystems: a generative ai-enabled federated learning-based trust management approach. IEEE Internet of Things Journal 12 (10), p. 13353–13366. External Links: Document, ISSN 2327-4662 Cited by: §2, Table 8. Dong et al. (2023) C. Dong, S. Pal, Q. An, A. Yao, F. Jiang, Z. Xu, J. Li, M. Lu, Y. Song, S. Chen, and X. Liu Securing smart uav delivery systems using zero trust principle -driven blockchain architecture. In 2023 IEEE International Conference on Blockchain, Blockchain, IEEE International Conference on Blockchain, p. 315–322. External Links: Document, ISBN 979-8-3503-1929-3 Cited by: §2.1. D’aniello and Fotia (2025) G. D’aniello and L. Fotia Blockchain and ai-based methods for trust management in iot: a comprehensive survey. Internet of Things, p. 101755. Cited by: §1.1, Table 1, §2.1, §5.1.1. Egala et al. (2023) B. S. Egala, A. K. Pradhan, P. Dey, V. Badarla, and S. P. Mohanty Fortified-chain 2.0: intelligent blockchain for decentralized smart healthcare system. IEEE Internet of Things Journal 10 (14), p. 12308–12321. External Links: Document, ISSN 2327-4662 Cited by: Table 11, §5.1.1. El Majdoubi et al. (2020) D. El Majdoubi, H. El Bakkali, and S. Sadki Towards smart blockchain-based system for privacy and security in a smart city environment. In Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (Cloudtech’20), M. Essaaidi, M. Zbakh, and A. Ouacha (Eds.), p. 117–123. External Links: Document, ISBN 978-1-7281-6175-4 Cited by: Table 11, Table 12. Fang et al. (2020) H. Fang, A. Qi, and X. Wang Fast authentication and progressive authorization in large-scale iot: how to leverage ai for security enhancement. IEEE Network 34 (3), p. 24–29. External Links: Document, ISSN 0890-8044 Cited by: §2, Table 11, Table 12. Faraji et al. (2024) F. Faraji, A. Javadpour, A. K. Sangaiah, and H. Zavieh A solution for resource allocation through complex systems in fog computing for the internet of things. Computing 106 (7, SI), p. 2107–2131. External Links: Document, ISSN 0010-485X Cited by: Table 11, §5.1.2. Farooq et al. (2022) U. Farooq, M. Asim, N. Tariq, T. Baker, and A. I. Awad Multi-mobile agent trust framework for mitigating internal attacks and augmenting rpl security. Sensors 22 (12), p. 4539. Cited by: Table 11, §5.1.1. Feng et al. (2023) X. Feng, J. Wu, Y. Wu, J. Li, and W. Yang Blockchain and digital twin empowered trustworthy self-healing for edge-ai enabled industrial internet of things. Information Sciences 642. External Links: Document, ISSN 0020-0255 Cited by: §1, Table 11, Table 7, §4, §4, §5.1.1, §5.1.3. Fortino et al. (2023) G. Fortino, L. Fotia, F. Messina, D. Rosaci, and G. M. L. Sarne A social edge-based iot framework using reputation-based clustering for enhancing competitiveness. IEEE Transactions on Computational Social Systems 10 (4), p. 2051–2060. External Links: Document, ISSN 2329-924X Cited by: §2.2.2, Table 11, Table 12, Table 5, §4, §4. Gheisari et al. (2025) M. Gheisari, H. Tahaei, C. Fernandez-Campusano, M. Malik, E. Mnkandla, Z. Wang, M. Sibiya, J. L. Webber, M. R. Mughal, C. Lee, P. Sun, and A. Mehbodniya A flexible software-defined networking-based privacy-preserving method for internet of things-based smart city environment based on the neighbors situation. Computer 58 (5), p. 27–36. External Links: Document, ISSN 0018-9162 Cited by: §2, Table 11, Table 11, §5.1.3. Gnanajeyaraman et al. (2023) R. Gnanajeyaraman, U. Arul, G. Michael, A. Selvakumar, S. Ramesh, and T. Manikandan VANET security enhancement in cloud navigation with internet of things-based trust model in deep learning architecture. Soft Computing. External Links: Document, ISSN 1432-7643 Cited by: Table 11, Table 11, §5.1.1. Guo et al. (2022a) J. Guo, A. Liu, K. Ota, M. Dong, X. Deng, and N. Xiong ITCN: an intelligent trust collaboration network system in iot. IEEE Transactions on Network Science and Engineering 9 (1), p. 203–218. External Links: Document, ISSN 2327-4697 Cited by: §2.2.1, Table 11, Table 12, Table 6, §4, §5.1.2. Guo et al. (2022b) S. Guo, Y. Qi, Y. Jin, W. Li, X. Qiu, and L. Meng Endogenous trusted drl-based service function chain orchestration for iot. IEEE Transactions on Computers 71 (2), p. 397–406. External Links: Document, ISSN 0018-9340 Cited by: §1, Table 11, Table 6, §4, §4, §4. Hammad and Trilles (2025) S. S. Hammad and S. Trilles Anomaly detection for trust management in internet of things systems. In Distributed Computing and Artificial Intelligence, Special Sessions I, 21st International Conference, G. Marreiros, L. Grande, J. Llerena, L. Conceicao, H. Ko, M. Plaza, and M. Ricca (Eds.), Lecture Notes in Networks and Systems, Vol. 1151, p. 286–291. External Links: Document, ISSN 2367-3370, ISBN 978-3-031-80945-3; 978-3-031-80946-0 Cited by: §2.2.2. Han et al. (2024) G. Han, Y. Huang, Y. He, F. Li, A. Li, and J. Peng A data transmission scheme based on reinforcement-learning-aided two-stage trust evaluation for uasns. IEEE Internet of Things Journal 11 (21), p. 35155–35166. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 11, Table 12, Table 9, §4. Hasan et al. (2024) H. R. Hasan, K. Salah, R. Jayaraman, I. Yaqoob, and M. Omar NFTs for combating deepfakes and fake metaverse digital contents. Internet of Things 25, p. 101133. External Links: Document, ISSN 2543-1536 Cited by: §5.2.4. Haseeb et al. (2022) K. Haseeb, T. Saba, A. Rehman, Z. Ahmed, H. H. Song, and H. H. Wang Trust management with fault-tolerant supervised routing for smart cities using internet of things. IEEE Internet of Things Journal 9 (22), p. 22608–22617. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 12, Table 9. Hassan et al. (2024) J. Hassan, A. Sohail, A. I. Awad, and M. A. Zaka LETM-iot: a lightweight and efficient trust mechanism for sybil attacks in internet of things networks. Ad Hoc Networks 163. External Links: Document, ISSN 1570-8705 Cited by: Table 11. Hossain et al. (2024) M. Hossain, G. Kayas, R. Hasan, A. Skjellum, S. Noor, and S. R. Islam A holistic analysis of internet of things (iot) security: principles, practices, and new perspectives. Future Internet 16 (2), p. 40. Cited by: §1, §2.3, §5.1.3. Huang et al. (2025) M. Huang, A. Liu, N. N. Xiong, and A. V. Vasilakos Towards intelligent and trustworthy task assignments for 5g-enabled industrial communication systems. Digital Communications and Networks 11 (1), p. 246–255. External Links: Document, ISSN 2468-5925 Cited by: §2.2.1, Table 11, Table 12. Islam et al. (2025) R. Islam, R. Bose, S. Roy, A. A. Khan, S. Sutradhar, S. Das, F. Ali, and A. A. Alzubi Decentralized trust framework for smart cities: a blockchain-enabled cybersecurity and data integrity model. Scientific Reports 15 (1). External Links: Document, ISSN 2045-2322 Cited by: Table 12. Ismail et al. (2024) S. Ismail, M. Nouman, D. W. Dawoud, and H. Reza Towards a lightweight security framework using blockchain and machine learning. Blockchain-Research and Applications 5 (1), p. 100174. External Links: Document, ISSN 2096-7209 Cited by: §2.2.3, Table 12, Table 8, §5.1.1. Jmal et al. (2025) R. Jmal, M. Masmoudi, I. Amous Ben Amor, and F. Sèdes Blockchain-powered trust management methodology in siot: a survey. Peer-to-Peer Networking and Applications 18 (4), p. 194. Cited by: §1.1, §1.1, Table 1, §5.1.1. Kamble et al. (2024) S. Kamble, S. Mahajan, D. D. Sapkal, and K. Ramteke Research challenges and opportunities for trust management systems in iot networks.. Grenze International Journal of Engineering & Technology (GIJET) 10. Cited by: §1.1, Table 1, §2.2.1, §5.1.4. Kaur and Kakkar (2024) G. Kaur and D. Kakkar DRIVE: dual rider-remora optimization for vehicular routing. Peer-to-Peer Networking and Applications 17 (2), p. 834–857. External Links: Document, ISSN 1936-6442 Cited by: Table 11, Table 11, Table 9, §4. Khan et al. (2025a) A. R. Khan, K. A. Awan, F. F. Alruwaili, A. Ara, H. H. Song, and T. Saba Trust-enhanced lightweight security framework for resource-constrained intelligent iot systems. IEEE Internet of Things Journal 12 (8), p. 10175–10182. External Links: Document, ISSN 2327-4662 Cited by: Table 11. Khan et al. (2024) M. A. Khan, R. N. Bin Rais, O. Khalid, and S. Ahmad Trust-based optimized reporting for detection and prevention of black hole attacks in low-power and lossy green iot networks. Sensors 24 (6), p. 1775. External Links: Document Cited by: Table 11. Khan et al. (2025b) S. Khan, N. Imtiaz, A. Kumar Biswas, Z. Bin Siddique, and Q. Ali Khan An expert hybrid federated learning and trust management for security, efficiency, and power optimization in smart health systems. IEEE Access 13, p. 58191–58210. External Links: Document, ISSN 2169-3536 Cited by: §2.2.1, §2.2.3, §2, Table 11, Table 12, §5.1.1. Khoshvaght et al. (2025) P. Khoshvaght, M. Alhussein, K. Aurangzeb, M. S. Yousefpoor, J. Lansky, and M. Hosseinzadeh An intelligent fuzzy logic based-trust system in underwater acoustic sensor networks. Engineering Applications of Artificial Intelligence 159 (B). External Links: Document, ISSN 0952-1976 Cited by: §2.2, Table 12. Konsta et al. (2022) A. M. Konsta, A. L. Lafuente, and N. Dragoni Trust management for internet of things: a systematic literature review. arXiv preprint arXiv:2211.01712. Cited by: §1.1, Table 1, §2.1, §2.2.1, §2.3, §4.2, §5.1.1, §5.1.1, §5.1.2. Kumar and Sharma (2025) R. Kumar and R. Sharma AI-driven dynamic trust management and blockchain-based in industrial iot. Computers & Electrical Engineering 123 (C). External Links: Document, ISSN 0045-7906 Cited by: §2.2.3, §2.2.4, Table 11, Table 12, Table 7, §4, §5.1.2. Kumari et al. (2020) A. Kumari, R. Gupta, S. Tanwar, and N. Kumar Blockchain and ai amalgamation for energy cloud management: challenges, solutions, and future directions. Journal of Parallel and Distributed Computing 143, p. 148–166. External Links: Document, ISSN 0743-7315 Cited by: §2.1. Kuppusamy and Murugesan (2024) R. Kuppusamy and A. Murugesan IoT-based external attacks aware secure healthcare framework using blockchain and sb-rnn-nvs-fu techniques. Technology and Health Care 32 (4), p. 2711–2731. External Links: Document, ISSN 0928-7329 Cited by: §2.2.3, Table 11, Table 11, Table 5, §4, §4. Lakhan et al. (2024) A. Lakhan, M. A. Mohammed, D. A. Zebari, K. H. Abdulkareem, M. Deveci, H. A. Marhoon, J. Nedoma, and R. Martinek Augmented iot cooperative vehicular framework based on distributed deep blockchain networks. IEEE Internet of Things Journal 11 (22), p. 35825–35838. External Links: Document, ISSN 2327-4662 Cited by: §2.1. Latif et al. (2022) R. Latif, M. U. Ahmed, S. Tahir, S. Latif, W. Iqbal, and A. Ahmad A novel trust management model for edge computing. Complex & Intelligent Systems 8 (5, SI), p. 3747–3763. External Links: Document, ISSN 2199-4536 Cited by: §2.2.4, §2.2, Table 11, Table 12, Table 6, §4, §4, §4, §4. Latif et al. (2023) R. Latif, B. M. Yakubu, and T. Saba MarketTrust: blockchain-based trust evaluation model for siot-based smart marketplaces. Scientific Reports 13 (1), p. 11571. External Links: Document, ISSN 2045-2322 Cited by: §2.2.2. Le and Shetty (2022) T. Le and S. Shetty Artificial intelligence-aided privacy preserving trustworthy computation and communication in 5g-based iot networks. Ad Hoc Networks 126, p. 102752. External Links: Document, ISSN 1570-8705 Cited by: Table 11, Table 12, Table 5, §4, §4, §4, §5.1.2. Lenard et al. (2023) T. Lenard, A. Collen, M. Benyahya, N. A. Nijdam, and B. Genge Exploring trust modeling and management techniques in the context of distributed wireless networks: a literature review. IEEE access 11, p. 106803–106832. Cited by: Table 1. Li et al. (2023a) C. Li, H. Yang, Z. Sun, Q. Yao, B. Bao, J. Zhang, and A. V. V. Vasilakos Federated hierarchical trust-based interaction scheme for cross-domain industrial iot. IEEE Internet of Things Journal 10 (1), p. 447–457. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 12, Table 7, §4, §4. Li et al. (2024a) J. Li, Z. Qin, W. Liu, and X. Yu Energy-aware and trust-collaboration cross-domain resource allocation algorithm for edge-cloud workflows. IEEE Internet of Things Journal 11 (4), p. 7249–7264. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 9, §4. Li et al. (2023b) W. Li, C. Stidsen, and T. Adam A blockchain-assisted security management framework for collaborative intrusion detection in smart cities. Computers & Electrical Engineering 111 (A), p. 108884. External Links: Document, ISSN 0045-7906 Cited by: Table 8. Li et al. (2020) W. Li, J. Tan, and Y. Wang A framework of blockchain-based collaborative intrusion detection in software defined networking. In Network and System Security, NSS 2020, M. Kutylowski, J. Zhang, and C. Chen (Eds.), Lecture Notes in Computer Science, Vol. 12570, p. 261–276. External Links: Document, ISSN 0302-9743, ISBN 978-3-030-65745-1; 978-3-030-65744-4 Cited by: Table 11, Table 12. Li et al. (2024b) Y. Li, Y. Cao, Y. Zhuang, J. Li, G. Du, and J. Chen Blockchain-enabled trust management with location privacy preservation in vehicular ad hoc networks. IEEE Internet of Things Journal 11 (14), p. 24659–24671. External Links: Document, ISSN 2327-4662 Cited by: §1, §2.2.4, Table 11, Table 8, §4, §4. Liang et al. (2024) H. Liang, L. Zhu, and F. R. Yu Collaborative edge intelligence service provision in blockchain empowered urban rail transit systems. IEEE Internet of Things Journal 11 (2), p. 2211–2223. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 11, Table 6, §4, §4, §4. Lu et al. (2025) D. Lu, G. Zhang, Y. Guo, and X. Jia Towards a trust ecosystem for crowdsourcing iot services: a macro perspective. IEEE Transactions on Services Computing 18 (5), p. 3292–3306. External Links: Document, ISSN 1939-1374 Cited by: Table 11. Magdich et al. (2022) R. Magdich, H. Jemal, and M. Ben Ayed A resilient trust management framework towards trust related attacks in the social internet of things. Computer Communications 191, p. 92–107. External Links: Document, ISSN 0140-3664 Cited by: §2.2. Mahmood et al. (2025a) S. Mahmood, M. Gohar, O. A. Khashan, N. Alzahrani, A. Ghani, and F. Al-Turjman Securing edge devices in iot and 6g: a trust-based approach for resource-constrained environments. IEEE Open Journal of the Communications Society 6, p. 6568–6610. External Links: Document Cited by: §5.2.2. Mahmood et al. (2025b) S. Mahmood, M. Gohar, S. Koh, M. U. Tariq, and A. Ghani Application level trust authority (appleta) for resource-constrained edge devices in iot and 6g. IEEE Transactions on Consumer Electronics 71 (2), p. 4934–4948. External Links: Document, ISSN 0098-3063 Cited by: Table 12, Table 5, §4, §4, §5.1.3. Marche and Nitti (2020) C. Marche and M. Nitti Trust-related attacks and their detection: a trust management model for the social iot. IEEE Transactions on Network and Service Management 18 (3), p. 3297–3308. Cited by: §1.1, §1.1, Table 1, §2.2.1, §2.2.1, §2.2, §2.3. Messina et al. (2025a) F. Messina, D. Rosaci, and G. M. L. Sarne A neural-symbolic approach to extract trust patterns in iot scenarios. Future Internet 17 (3), p. 116. External Links: Document, ISSN 1999-5903 Cited by: §1, §2.1, §2.2.3. Messina et al. (2025b) F. Messina, D. Rosaci, and G. M. L. Sarne Forming teams of smart objects to support mobile edge computing for iot-based connected vehicles. Applied Sciences-Basel 15 (17). External Links: Document Cited by: §2.2.1, Table 11, §5.1.2. Moeinaddini et al. (2025) E. Moeinaddini, E. Nazemi, and A. Shahraki A new approach on self-adaptive trust management for social internet of things. Computer Networks 263. External Links: Document, ISSN 1389-1286 Cited by: §2.2.2, §2.2, §2.3, §4.2, Table 12, Table 5, §4, §4, §5.1.3. Mohanta et al. (2025) B. K. Mohanta, A. I. Awad, T. Elsaka, H. Kheddar, and E. Baraka Smart-contract-based blockchain-enabled decentralized scheme for improving smart-grid security. Internet of Things 34. External Links: Document, ISSN 2543-1536 Cited by: §1, Table 9, §4. Moudoud et al. (2022) H. Moudoud, Z. Mlika, L. Khoukhi, and S. Cherkaoui Detection and prediction of fdi attacks in iot systems via hidden markov model. IEEE Transactions on Network Science and Engineering 9 (5), p. 2978–2990. External Links: Document, ISSN 2327-4697 Cited by: §2.2.2, §4.2, Table 11, Table 11, Table 12, Table 5, §4, §4. Muzammal et al. (2020) S. M. Muzammal, R. K. Murugesan, and N. Z. Jhanjhi A comprehensive review on secure routing in internet of things: mitigation methods and trust-based approaches. IEEE Internet of Things Journal 8 (6), p. 4186–4210. Cited by: §1.1, §1.1, Table 1, §5.1.1, §5.1.3. Najib et al. (2019) W. Najib S. Sulistyo et al. Survey on trust calculation methods in internet of things. Procedia Computer Science 161, p. 1300–1307. Cited by: §1.1, Table 1, §1. Narang and Kar (2021) N. Narang and S. Kar A hybrid trust management framework for a multi-service social iot network. Computer Communications 171, p. 61–79. External Links: Document, ISSN 0140-3664 Cited by: §2.2. Nashiruddin and Hidayati (2019) M. I. Nashiruddin and A. Hidayati Coverage and capacity analysis of lora wan deployment for massive iot in urban and suburban scenario. In 2019 5th International Conference on Science and Technology (ICST), Vol. 1, p. 1–6. Cited by: §2.1. Nawshin et al. (2024) F. Nawshin, D. Unal, M. Hammoudeh, and P. N. Suganthan AI-powered malware detection with differential privacy for zero trust security in internet of things networks. Ad Hoc Networks 161. External Links: Document, ISSN 1570-8705 Cited by: §2. Osamy et al. (2025) W. Osamy, O. Alfawaz, A. M. Khedr, and A. Aziz TAGSCS: trust aware data gathering technique based slicing and compressive sensing for iot based wsn. Ad Hoc Networks 178. External Links: Document, ISSN 1570-8705 Cited by: §2.2.1, Table 11, §5.1.3. Padmavathi and Saminathan (2025) V. Padmavathi and R. Saminathan A federated edge intelligence framework with trust based access control for secure and privacy preserving iot systems. Scientific Reports 15 (1). External Links: Document, ISSN 2045-2322 Cited by: Table 11. Patnaik et al. (2023) A. Patnaik, B. Mallik, and M. V. Krishna Blockchain based holistic trust management protocol for ubiquitous and pervasive iot network. Journal of Experimental & Theoretical Artificial Intelligence 35 (5), p. 629–648. External Links: Document, ISSN 0952-813X Cited by: Table 11, Table 12. Pourghebleh et al. (2019) B. Pourghebleh, K. Wakil, and N. J. Navimipour A comprehensive study on the trust management techniques in the internet of things. IEEE Internet of Things Journal 6 (6), p. 9326–9337. Cited by: §1.1, §1.1, Table 1. Qureshi and Shandilya (2022) S. G. Qureshi and S. K. Shandilya Nature-inspired adaptive decision support system for secured clustering in cyber networks. Multimedia Tools and Applications. External Links: Document, ISSN 1380-7501 Cited by: Table 11, Table 11, Table 12, Table 9, §4. Rahman et al. (2023) M. A. Rahman, A. Dahroug, and S. M. Moussa Using artificial intelligence for trust management systems in fog computing: a comprehensive study. In Progress in Artificial Intelligence, EPIA 2023, PT I, N. Moniz, Z. Vale, J. Cascalho, C. Silva, and R. Sebastiao (Eds.), Lecture Notes in Artificial Intelligence, Vol. 14116, p. 453–466. External Links: Document, ISSN 2945-9133, ISBN 978-3-031-49010-1; 978-3-031-49011-8 Cited by: §2.1. Rajendran and Jebakumar (2022) S. Rajendran and R. Jebakumar Friendliness based trustworthy relationship management (f-trm) in social internet of things. Wireless Personal Communications 123 (3), p. 2625–2647. External Links: Document, ISSN 0929-6212 Cited by: §2.2.3. Ranathunga et al. (2021) T. Ranathunga, A. McGibney, and S. Rea The convergence of blockchain and machine learning for decentralized trust management in iot ecosystems. In Proceedings of the 2021 the 19th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2021, p. 499–504. External Links: Document, ISBN 978-1-4503-9097-2 Cited by: §1, §1, Table 12, §5.1.1. Rathee et al. (2025) G. Rathee, H. Saini, S. P. Chakkravarthy, and R. Maheswar An intelligent and trust-enabled farming systems with blockchain and digital twins on mobile edge computing. International Journal of Network Management 35 (1). External Links: Document, ISSN 1055-7148 Cited by: Table 11, Table 6, §4, §4, §4. Ratnayake et al. (2024) R. Ratnayake, M. Liyanage, and L. Murphy Machine learning for data trust evaluations in blockchain-enabled iot systems. In 2024 IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024, IEEE International Conference on Blockchain and Cryptocurrency. External Links: Document, ISSN 2832-8892, ISBN 979-8-3503-1675-9; 979-8-3503-1674-2 Cited by: Table 11, Table 11, Table 7, §4, §4. Reddy et al. (2025) M. V. K. Reddy, S. B. Krishnan, A. Shaik, and P. Chakrabarti AI-integrated adaptive manet framework for iot-driven healthcare systems: enhancing scalability, security, and real-time communication. European Physical Journal Plus 140 (9). External Links: Document, ISSN 2190-5444 Cited by: Table 11, §5.1.1, §5.1.3. Rehman et al. (2025) A. Rehman, K. A. Awan, A. Al-Rasheed, A. Ara, F. F. Alruwaili, S. Al-Otaibi, and T. Saba A novel hybrid fuzzy logic and federated learning framework for enhancing cybersecurity and fraud detection in iot-enabled metaverse transactions. Egyptian Informatics Journal 30. External Links: Document, ISSN 1110-8665 Cited by: §2.2.2, Table 11, Table 12, Table 5, §4, §4, §4, §5.1.2. Sagar et al. (2024) S. Sagar, A. Mahmood, Q. Z. Sheng, W. E. Zhang, Y. Zhang, and J. K. Pabani Understanding the trustworthiness management in the social internet of things: a survey. Computer Networks 251, p. 110611. Cited by: §1.1, §1.1, Table 1, §1, §2.2.1, §2.3. Salim et al. (2022) A. Salim, W. Osamy, A. Aziz, and A. M. Khedr SEEDGT: secure and energy efficient data gathering technique for iot applications based wsns. Journal of Network and Computer Applications 202, p. 103353. External Links: Document, ISSN 1084-8045 Cited by: Table 11, Table 11, Table 12, Table 6, §4, §4, §4. Sasikumar et al. (2023) A. Sasikumar, S. Vairavasundaram, K. Kotecha, V. Indragandhi, L. Ravi, G. Selvachandran, and A. Abraham Blockchain-based trust mechanism for digital twin empowered industrial internet of things. Future Generation Computer Systems-The International Journal of eScience 141, p. 16–27. External Links: Document, ISSN 0167-739X Cited by: §5.2.4. Sharma et al. (2025) S. Sharma, M. Yahya, A. Jain, A. I. Alutaibi, A. Baihan, P. Dutta, and A. Abugabah Evaluation of legitimacy of iot devices based on an energy-efficient trust management scheme in information-centric networking. International Journal of Communication Systems 38 (9). External Links: Document, ISSN 1074-5351 Cited by: §2.2.1, §2.2.4, Table 11. Sharma et al. (2024) V. Sharma, R. Beniwal, and V. Kumar Multi-level trust-based secure and optimal iot-wsn routing for environmental monitoring applications. Journal of Supercomputing 80 (8), p. 11338–11381. External Links: Document, ISSN 0920-8542 Cited by: §2.2.1, Table 11, Table 11, Table 12, Table 6, §4, §4, §4. Sheng et al. (2024) X. Sheng, Y. Zhou, and X. Cui Graph neural network based asynchronous federated learning for digital twin-driven distributed multi-agent dynamical systems. Mathematics 12 (16). External Links: Document Cited by: §5.2.3. Shirvani and Masdari (2023) M. H. Shirvani and M. Masdari A survey study on trust-based security in internet of things: challenges and issues. Internet of Things 21, p. 100640. Cited by: §1.1, §1.1, Table 1. Singh and Park (2023) S. K. Singh and J. H. Park TaLWaR: blockchain-based trust management scheme for smart enterprises with augmented intelligence. IEEE Transactions on Industrial Informatics 19 (1), p. 626–634. External Links: Document, ISSN 1551-3203 Cited by: Table 12. Sinha et al. (2024) A. Sinha, S. Singh, and H. K. Verma AI-driven task scheduling strategy with blockchain integration for edge computing. Journal of Grid Computing 22 (1), p. 13. External Links: Document, ISSN 1570-7873 Cited by: §2.2.2. Souri et al. (2024) A. Souri, Y. Zhao, M. Gao, A. Mohammadian, J. Shen, and E. Al-Masri A trust-aware and authentication-based collaborative method for resource management of cloud-edge computing in social internet of things. IEEE Transactions on Computational Social Systems 11 (4), p. 4899–4908. External Links: Document, ISSN 2329-924X Cited by: §2.1, Table 11, Table 11. Sun et al. (2025) P. Sun, Y. Wan, Z. Wu, Z. Fang, and Q. Li A survey on privacy and security issues in iot-based environments: technologies, protection measures and future directions. Computers & Security 148, p. 104097. Cited by: §2.1. Sun et al. (2024) W. Sun, G. Peng, W. Pan, J. Deng, X. Cui, and F. Lin Trust management as a service for rfid based applications in edge enabled cloud. In Computer Networks and IoT, PT 3, IAIC 2023, H. Jin, Y. Pan, and J. Lu (Eds.), Communications in Computer and Information Science, Vol. 2060, p. 44–55. External Links: Document, ISSN 1865-0929, ISBN 978-981-97-1331-8; 978-981-97-1332-5 Cited by: §2.1, Table 11, Table 6, §4, §4, §4, §4. Sylla et al. (2021) T. Sylla, M. A. Chalouf, F. Krief, and K. Samake SETUCOM: secure and trustworthy context management for context-aware security and privacy in the internet of things. Security and Communication Networks 2021, p. 6632747. External Links: Document, ISSN 1939-0114 Cited by: Table 12, §5.1.2. Tadj et al. (2023) T. Tadj, R. Arablouei, and V. Dedeoglu On evaluating iot data trust via machine learning. Future Internet 15 (9), p. 309. External Links: Document, ISSN 1999-5903 Cited by: §5.1.2, §5.1.4. Takemura et al. (2024) T. Takemura, R. Yamamoto, and K. Suzaki TEE-pa: tee is a cornerstone for remote provenance auditing on edge devices with semi-tcb. IEEE Access 12, p. 26536–26549. External Links: Document, ISSN 2169-3536 Cited by: §5.2.1. Tsekenis et al. (2025) V. Tsekenis, S. Barmpounakis, and P. Demestichas Flexible topologies for efficient network coverage expansion, sustainability and trust. In 2025 IEEE Wireless Communications and Networking Conference, WCNC, IEEE (Ed.), IEEE Wireless Communications and Networking Conference. External Links: Document, ISSN 1525-3511, ISBN 979-8-3503-6837-6; 979-8-3503-6836-9 Cited by: §2.2, Table 11, Table 6, §4, §4. Tuncel and Oztoprak (2025) Y. K. Tuncel and K. Oztoprak SAFE-cast: secure ai-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication. PeerJ Computer Science 11. External Links: Document Cited by: §2.2.2, Table 11, Table 6, §4, §4, §4. Turkina and Ihnatiev (2020) V. Turkina and D. Ihnatiev Approach to sustainable trust and reputation evaluation in distributed mobile networks of the internet of things. In 2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT): IoT, Big Data and AI for a Safe & Secure World and Industry 4.0, p. 117–121. External Links: Document, ISBN 978-1-7281-9957-3 Cited by: §2.1. Tyagi et al. (2023) H. Tyagi, R. Kumar, and S. K. Pandey A detailed study on trust management techniques for security and privacy in iot: challenges, trends, and research directions. High-Confidence Computing 3 (2), p. 100127. Cited by: Table 1, §2.1, §2.3. Verma and Chandra (2023) R. Verma and S. Chandra RepuTE: a soft voting ensemble learning framework for reputation-based attack detection in fog-iot milieu. Engineering Applications of Artificial Intelligence 118, p. 105670. External Links: Document, ISSN 0952-1976 Cited by: §1, §1, Table 11, Table 12. Wang et al. (2024) B. Wang, J. Peng, and M. Cui Secure access technology for industrial internet of things. Concurrency and Computation-Practice & Experience 36 (25). External Links: Document, ISSN 1532-0626 Cited by: §2.2.3, Table 11, Table 11, Table 7, §4. Wang et al. (2025a) B. Wang, J. Wang, and M. Li A method for trust-based collaborative smart device selection and resource allocation in the financial internet of things. Sensors 25 (13). External Links: Document Cited by: §2.2.1, Table 11, Table 6, §4, §4, §4. Wang et al. (2025b) C. Wang, J. Zhang, S. Zhang, and X. Wu Trust evaluation mechanism for data collection in smart cities. Discover Computing 28 (1). External Links: Document, ISSN 2948-2984 Cited by: Table 11, Table 8. Wang et al. (2023a) C. Wang, Z. Cai, D. Seo, and Y. Li TMETA: trust management for the cold start of iot services with digital-twin-aided blockchain. IEEE Internet of Things Journal 10 (24), p. 21337–21348. External Links: Document, ISSN 2327-4662 Cited by: §5.1.2. Wang et al. (2022a) J. Wang, Z. Yan, H. Wang, T. Li, and W. Pedrycz A survey on trust models in heterogeneous networks. IEEE Communications Surveys & Tutorials 24 (4), p. 2127–2162. Cited by: §1.1, Table 1. Wang et al. (2020) T. Wang, H. Luo, W. Jia, A. Liu, and M. Xie MTES: an intelligent trust evaluation scheme in sensor-cloud-enabled industrial internet of things. IEEE Transactions on Industrial Informatics 16 (3), p. 2054–2062. External Links: Document, ISSN 1551-3203 Cited by: §1, Table 12, Table 7, §4. Wang et al. (2023b) T. Wang, B. Sun, L. Wang, X. Zheng, and W. Jia EIDLS: an edge-intelligence-based distributed learning system over internet of things. IEEE Transactions on Systems Man Cybernetics-Systems 53 (7), p. 3966–3978. External Links: Document, ISSN 2168-2216 Cited by: Table 11, Table 12, §5.1.1. Wang et al. (2021) T. Wang, P. Wang, S. Cai, X. Zheng, Y. Ma, W. Jia, and G. Wang Mobile edge-enabled trust evaluation for the internet of things. Information Fusion 75, p. 90–100. External Links: Document, ISSN 1566-2535 Cited by: Table 11, Table 12, §5.1.4. Wang et al. (2026) Y. Wang, A. Mahmood, M. F. M. Sabri, and H. Zen Trust management in the internet of vehicles: a survey of the state-of-the-art. IEEE Open Journal of Intelligent Transportation Systems. Cited by: §1.1, Table 1. Wang et al. (2022b) Y. Wang, H. Zen, M. F. M. Sabri, X. Wang, and L. C. Kho Towards strengthening the resilience of iov networks-a trust management perspective. Future Internet 14 (7), p. 202. External Links: Document Cited by: §5.1.2. Wang et al. (2025c) Z. Wang, Y. Fu, and X. Lin Attribute-based bilateral access control with sanitization and trust management for iiot. IEEE Internet of Things Journal 12 (8), p. 10818–10833. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 12, Table 7, §4. Wazid et al. (2022) M. Wazid, A. K. Das, and S. Shetty TACAS-iot: trust aggregation certificate-based authentication scheme for edge-enabled iot systems. IEEE Internet of Things Journal 9 (22), p. 22643–22656. External Links: Document, ISSN 2327-4662 Cited by: Table 11, §5.1.1, §5.1.3. Xiang et al. (2025a) X. Xiang, J. Cao, and W. Fan Secure authentication and trust management scheme for edge ai-enabled cyber-physical systems. IEEE Transactions on Intelligent Transportation Systems 26 (3), p. 3237–3249. External Links: Document, ISSN 1524-9050 Cited by: Table 11, Table 12, Table 8, §4, §5.1.2. Xiang et al. (2025b) Z. Xiang, J. Cheng, C. Liu, Q. Mao, G. Yuan, and S. Gao Privacy-preserving autonomous vehicle group formation in a collusive attack scenario. IEEE Internet of Things Journal 12 (13), p. 25576–25586. External Links: Document, ISSN 2327-4662 Cited by: Table 11. Xiong et al. (2023) R. Xiong, W. Ren, X. Hao, J. He, and K. R. Choo BDIM: a blockchain-based decentralized identity management scheme for large scale internet of things. IEEE Internet of Things Journal 10 (24), p. 22581–22590. External Links: Document, ISSN 2327-4662 Cited by: §2.2.3, §5.1.1. Xu et al. (2026) Q. Xu, L. Zhang, Y. Liu, and Z. Li Enhancing trust management system for connected autonomous vehicles using machine learning methods: a survey. IEEE Transactions on Intelligent Transportation Systems 27 (4), p. 3862–3893. External Links: Document Cited by: §1.1, §1.1, Table 1. Yang et al. (2025) Y. Yang, H. Lei, and Q. Zhu PRADA: proactive risk assessment and mitigation of misinformed demand attacks on navigational route recommendations. IEEE Transactions on Information Forensics and Security 20, p. 10879–10892. External Links: Document, ISSN 1556-6013 Cited by: §2.3, Table 12. Yang et al. (2023) Z. Yang, R. Wang, D. Wu, B. Yang, and P. Zhang Blockchain-enabled trust management model for the internet of vehicles. IEEE Internet of Things Journal 10 (14), p. 12044–12054. External Links: Document, ISSN 2327-4662 Cited by: §2.2.1, §2.2, Table 12, Table 8, §4, §5.1.1. Yu et al. (2023) C. Yu, G. Xia, L. Song, W. Peng, J. Chen, D. Zhang, and H. Li CET-aotm: cloud-edge-terminal collaborative trust evaluation scheme for aiot networks. In Service-Oriented Computing, ICSOC 2023, PT I, F. Monti, S. Rinderle-Ma, A. Cortes, Z. Zheng, and M. Mecella (Eds.), Lecture Notes in Computer Science, Vol. 14420, p. 143–158. External Links: Document, ISSN 0302-9743, ISBN 978-3-031-48423-0; 978-3-031-48424-7 Cited by: §2.2.1, Table 11, Table 12, Table 6, §4, §4, §4. Yu et al. (2024) Y. Yu, Q. Lu, and Y. Fu Dynamic trust management for the edge devices in industrial internet. IEEE Internet of Things Journal 11 (10), p. 18410–18420. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 12, Table 7, §4, §4. Zehra et al. (2024) S. Zehra, S. R. Rizvi, and S. El-Tawab A novel framework to safeguard inter-vehicular communication and privacy. In 2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIOT, p. 44–51. External Links: Document, ISBN 979-8-3315-0617-9; 979-8-3315-2941-3 Cited by: Table 11, Table 12. Zhang et al. (2021) C. Zhang, W. Li, Y. Luo, and Y. Hu AIT: an ai-enabled trust management system for vehicular networks using blockchain technology. IEEE Internet of Things Journal 8 (5), p. 3157–3169. External Links: Document, ISSN 2327-4662 Cited by: §2.2.2, Table 11, Table 12, Table 5, §4, §4, §4. Zhang et al. (2023) F. Zhang, H. Wang, L. Zhou, D. Xu, and L. Liu A blockchain-based security and trust mechanism for ai-enabled iiot systems. Future Generation Computer Systems-The International Journal of eScience 146, p. 78–85. External Links: Document, ISSN 0167-739X Cited by: Table 7, §4, §4. Zhang et al. (2024a) R. Zhang, A. Liu, T. Wang, N. N. Xiong, and A. V. Vasilakos A trust active and trace back based trust management system about effective data collection for mobile iot services. Information Sciences 664. External Links: Document, ISSN 0020-0255 Cited by: §2.1, §5.1.1. Zhang et al. (2024b) Y. Zhang, Y. Yu, W. Sun, and Z. Cao Towards an energy-aware two-way trust routing scheme in fog computing environments. Telecommunication Systems 87 (4), p. 973–989. External Links: Document, ISSN 1018-4864 Cited by: Table 11, Table 11, Table 12, Table 9. Zhang et al. (2024c) Y. Zhang, C. Jiang, and P. Zhang Security-aware resource allocation scheme based on drl in cloud-edge-terminal cooperative vehicular network. IEEE Internet of Things Journal 11 (1), p. 95–104. External Links: Document, ISSN 2327-4662 Cited by: Table 11, Table 9, §4. Zhu et al. (2024) X. Zhu, F. Ma, F. Ding, Z. Guo, J. Yang, and K. Yu A low-latency edge computation offloading scheme for trust evaluation in finance-level artificial intelligence of things. IEEE Internet of Things Journal 11 (1), p. 114–124. External Links: Document, ISSN 2327-4662 Cited by: Table 11.

Instant research discovery