cs.CRSep 21, 2026
SaveTrust in Edge-Enabled IoT Security: Features, Challenges and Research Directions
Organizations: Cyber Security and Privacy Research Lab., SPFLab, Department of Computer Engineering, Istanbul Technical University, Istanbul, Türkiye · Zurich University of Applied Sciences (ZHAW), Institute of Computer Science (InIT), Winterthur, Switzerland
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.
Figures & tables
| 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 | ||||
Table 1. Comparison of Existing Surveys on Trust Management in IoT with Our Work
Figure 1. Three-Layer Model of IoT Systems Across Consumer, Commercial, Industrial, and Infrastructure Domains Three-Layer Model of IoT Systems Across Consumer, Commercial, Industrial, and Infrastructure Domains
Figure 2. Conceptual Trust Management Process Conceptual Trust Management Process
| 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 |
Table 2. Architectural Comparison of Trust Management Placement
| 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 |
Table 3. Classification of Trust-Related Attacks by Target and Architectural IoT Layer
| 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 |
Table 4. Trust-Related Attacks Across The Trust Management Process in Figure 2
(a)
Figure 4. PRISMA Flow Diagram From Identification To Screening, Eligibility, And Inclusion Steps PRISMA Flow Diagram
Figure 5. Trust-Design Taxonomy for Classifying Trust Management Schemes Trust Design Taxonomy
Figure 6. Surveyed Literature Overview: (a) Distribution Across IoT Application Domains, and (b) Annual Distribution By Domain From 2020 to 2025
| 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 |
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? |
| ( 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 |
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? |
| ( 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 |
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? |
| ( 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 |
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? |
| ( 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 |
Table 9. Continue Tab. 6
| Design Item | Consumer IoT ( ) | Commercial IoT ( ) | Industrial IoT ( ) | Infrastructure IoT ( ) |
| 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. |
Table 10. Cross-Domain Comparison of Characteristic Trust-Design Choices ( )
| 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 ) |
Table 11. Taxonomy of Trust-Supported Decisions in IoT Operations
Figure 7. Distribution of Operational Decisions Supported by Trust Management in the Surveyed Literature Bar chart showing the distribution of operational decisions supported by trust management in the surveyed literature.
| 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 ) | ✓ | – | – | ✓ | – | – | – | – | – | – | ✓ | – | – | – | – | – |
Table 12. Mapping of Studies to Mitigated Trust-Related Attacks
Figure 8. Frequency Distribution of Specific Research Challenges Identified in the Surveyed Literature (N=55) Frequency distribution of research challenges in surveyed literature.
Figure 9. Research Challenge Trends in the Reviewed Literature by (a) Yearly Distribution and (b) Broad Areas
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