pith. sign in

arxiv: 2604.17179 · v1 · submitted 2026-04-19 · 💻 cs.CR · cs.AI· cs.NI

Decentralised Trust and Security Mechanisms for IoT Networks at the Edge: A Comprehensive Review

Khandoker Ashik Uz Zaman , Mahdi H. Miraz , Mohammed N. M. Ali This is my paper

Pith reviewed 2026-05-10 06:45 UTC · model grok-4.3

classification 💻 cs.CR cs.AIcs.NI
keywords IoTedge computingdecentralised securitytrust mechanismsblockchainfederated learningzero trustanomaly detection
0
0 comments X

The pith

Decentralised trust mechanisms for IoT edge networks enhance privacy and reduce single points of failure.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews thirty recent studies to evaluate decentralised alternatives to central control in IoT edge environments. It examines how approaches such as federated learning, Zero Trust architectures, and lightweight blockchain establish trust, enable secure communication, and support intrusion detection across resource-limited devices. A sympathetic reader would care because centralised systems create bottlenecks and single targets for attacks as IoT networks grow heterogeneous and widespread. The review concludes that decentralised designs deliver measurable gains in privacy preservation and adaptive threat handling while leaving open questions on scaling and compatibility.

Core claim

Analysis of the thirty studies shows that decentralised architectures establish trust through distributed models, support secure communication without central intermediaries, and enable anomaly detection via frameworks such as DFGL-LZTA, SecFedDNN and COSIER. These designs improve privacy by keeping data local, eliminate single points of failure, and allow faster responses to threats compared with centralised control, although they still encounter limits in scalability, efficiency, and interoperability across heterogeneous devices.

What carries the argument

Comparative review of decentralised trust and security mechanisms drawn from thirty studies, focusing on how federated learning, Zero Trust models, and lightweight blockchain handle trust establishment, secure communication, and anomaly detection in edge IoT settings.

If this is right

  • Decentralised designs keep data local and thereby strengthen privacy without relying on a central repository.
  • Absence of a single control point lowers the risk that one compromise brings down the entire network.
  • Distributed anomaly detection allows faster, more adaptive responses to intrusions than centralised monitoring.
  • Remaining gaps in scalability and interoperability indicate that full deployment requires further optimisation of the reviewed frameworks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Standardisation of interfaces between the different decentralised frameworks could accelerate practical adoption across vendors.
  • Testing the reviewed mechanisms under real-world conditions with thousands of devices would reveal whether efficiency gains hold at scale.
  • Combining lightweight blockchain with federated learning might address both trust and data-privacy needs in future edge deployments.

Load-bearing premise

The thirty selected studies give a sufficient and unbiased picture of current decentralised trust and security mechanisms for IoT edge networks.

What would settle it

A broader search that locates many additional high-quality studies showing centralised mechanisms achieve lower breach rates or better scalability in large IoT deployments would undermine the reported advantages of decentralised designs.

read the original abstract

INTRODUCTION: The proliferation of the amalgamation of IoT and edge computing has increased the demand for decentralised trust and security mechanisms capable of operating across heterogeneous and resource-limited devices. Approaches such as federated learning, Zero Trust architectures, lightweight blockchain and distributed neural models offer alternatives to centralised control. OBJECTIVES: This review examines various state-of-the-art decentralised mechanisms and evaluates their effectiveness in terms of securing IoT networks at the edge. METHODS: Thirty recent studies were analysed to compare how decentralised architectures establish trust, support secure communication and enable intrusion and anomaly detection. Frameworks, such as DFGL-LZTA, SecFedDNN and COSIER were assessed. RESULTS: Decentralised designs enhance privacy, reduce single points of failure and improve adaptive threat response, though challenges remain in scalability, efficiency and interoperability. CONCLUSION: The study identifies key considerations and future research needs for building secure and resilient trust-aware IoT edge ecosystems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. This manuscript is a literature review analyzing thirty recent studies on decentralized trust and security mechanisms for IoT networks at the edge. It evaluates frameworks such as DFGL-LZTA, SecFedDNN, and COSIER, claiming that decentralized designs enhance privacy, reduce single points of failure, and improve adaptive threat response, while noting ongoing challenges in scalability, efficiency, and interoperability.

Significance. If supported by a representative sample of studies, the review would offer significant value as a synthesis of decentralized approaches (including federated learning, zero-trust architectures, and lightweight blockchain) for resource-constrained IoT edge environments. It identifies practical benefits for privacy and threat response along with key considerations for future resilient trust-aware ecosystems.

major comments (2)
  1. [METHODS] METHODS (as described in the abstract): The paper states that 'Thirty recent studies were analysed' to compare decentralised architectures and assess frameworks like DFGL-LZTA, SecFedDNN and COSIER, but provides no details on search strategy, databases, keywords, date range, inclusion/exclusion criteria, screening process, or quality assessment. This omission is load-bearing for the RESULTS claim that 'Decentralised designs enhance privacy, reduce single points of failure and improve adaptive threat response', because without evidence of representativeness the synthesis could reflect selective inclusion rather than the state of the field.
  2. [RESULTS] RESULTS: The section presents only qualitative conclusions without any tabulated comparison of metrics (e.g., privacy gains, latency, or detection rates) or quantitative meta-summary across the thirty studies. This makes it impossible to trace the stated benefits and challenges directly to specific evidence, weakening the reliability of the cross-study synthesis.
minor comments (1)
  1. [Abstract] The abstract is formatted with explicit labels (INTRODUCTION, OBJECTIVES, METHODS, RESULTS, CONCLUSION), which is non-standard for journal abstracts and reduces readability; integrate into a single cohesive paragraph.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address the major comments point by point below, indicating the revisions we will implement to enhance the transparency and rigor of our literature review.

read point-by-point responses

  1. Referee: [METHODS] METHODS (as described in the abstract): The paper states that 'Thirty recent studies were analysed' to compare decentralised architectures and assess frameworks like DFGL-LZTA, SecFedDNN and COSIER, but provides no details on search strategy, databases, keywords, date range, inclusion/exclusion criteria, screening process, or quality assessment. This omission is load-bearing for the RESULTS claim that 'Decentralised designs enhance privacy, reduce single points of failure and improve adaptive threat response', because without evidence of representativeness the synthesis could reflect selective inclusion rather than the state of the field.

    Authors: We acknowledge the validity of this observation. The Methods section in the submitted manuscript is concise and does not detail the systematic review process. In the revised manuscript, we will expand this section to include a full description of the search strategy, including the academic databases consulted (IEEE Xplore, ACM Digital Library, Springer, Elsevier, and Google Scholar), specific keywords and Boolean combinations employed, the publication date range (2018-2024), inclusion and exclusion criteria, the multi-stage screening process, and any quality assessment applied to the selected studies. This will demonstrate that the thirty studies form a representative sample of recent work on decentralized trust and security mechanisms for IoT edge networks, thereby supporting the generalizability of our conclusions. revision: yes

  2. Referee: [RESULTS] RESULTS: The section presents only qualitative conclusions without any tabulated comparison of metrics (e.g., privacy gains, latency, or detection rates) or quantitative meta-summary across the thirty studies. This makes it impossible to trace the stated benefits and challenges directly to specific evidence, weakening the reliability of the cross-study synthesis.

    Authors: We agree that a more quantitative or structured presentation of the results would strengthen the paper. We will introduce a comprehensive summary table in the revised Results section that lists each of the thirty studies along with their primary decentralized approach, reported advantages (e.g., privacy improvements, elimination of single points of failure, adaptive detection capabilities), encountered limitations (scalability, efficiency, interoperability), and any specific performance metrics provided in the original works. While the diversity of the studies precludes a formal statistical meta-analysis, this table will enable direct tracing of our synthesized claims to the underlying evidence. We will also add a discussion of the challenges in cross-study comparison due to varying methodologies and metrics. revision: partial

Circularity Check

0 steps flagged

No circularity: literature review with no derivations or self-referential reductions

full rationale

The paper is a qualitative synthesis of thirty external studies on decentralized IoT edge security. It contains no equations, predictions, fitted parameters, uniqueness theorems, or ansatzes. The RESULTS section states conclusions ('Decentralised designs enhance privacy, reduce single points of failure...') as a summary of the analyzed works rather than a derivation that reduces to the paper's own inputs. No self-citation is load-bearing; all referenced frameworks (DFGL-LZTA, SecFedDNN, COSIER) are external. The selection methodology is described only at a high level, but this is a methodological limitation, not a circular reduction of any claimed result to its own premises. The derivation chain is absent, satisfying the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This literature review does not rely on free parameters, axioms, or invented entities as it does not present original derivations or models.

pith-pipeline@v0.9.0 · 5475 in / 1195 out tokens · 37209 ms · 2026-05-10T06:45:43.395342+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages

  1. [1]

    Cyber -security of centralized, decentralized, and distributed control -detector architectures for nonlinear processes

    Chen S, Wu Z, Christofides PD. Cyber -security of centralized, decentralized, and distributed control -detector architectures for nonlinear processes. Chemical Engineering Research and Design. 2021 Jan 1; 165:25-39

    work page 2021

  2. [2]

    Comparative Analysis of the Centralized and Decentralized Architecture of Cloud Computing in terms of Privacy Security

    Chen Y. Comparative Analysis of the Centralized and Decentralized Architecture of Cloud Computing in terms of Privacy Security. Applied and Computational Engineering. 2025 Apr 7; 145:51-6

    work page 2025

  3. [3]

    Comparative Analysis of Centralized and Federated Intrusion Detection in IoT -Enabled Cyber -Physical Systems Under Data and Label-Skew

    Khan MA, Rais RN, Khalid O, Deriche M. Comparative Analysis of Centralized and Federated Intrusion Detection in IoT -Enabled Cyber -Physical Systems Under Data and Label-Skew. IEEE Access. 2025 Sep 11; 13: 160767- 160785

    work page 2025

  4. [4]

    A decentralized trust establishment protocol for smart IoT systems

    El Majdoubi D, El Bakkali H, Bensaih M, Sadki S. A decentralized trust establishment protocol for smart IoT systems. Internet of Things. 2022 Nov 1; 20:100634

    work page 2022

  5. [5]

    Iot: a decentralized trust management system using blockchain -empowered federated learning

    Bi L, Muazu T, Samuel O. Iot: a decentralized trust management system using blockchain -empowered federated learning. Sustainability. 2022 Dec 26; 15[1]:374

    work page 2022

  6. [6]

    SecFedDNN: A Secure Federated Deep Learning Framework for Edge –Cloud Environments

    Alamir RH, Noor A, Almukhalfi H, Almukhlifi R, Noor TH. SecFedDNN: A Secure Federated Deep Learning Framework for Edge –Cloud Environments. Systems. 2025 Jun 12; 13[6]:463

    work page 2025

  7. [7]

    Optimized RNN -based performance prediction of IoT and WSN -oriented smart city application using improved honey badger algorithm

    Asha A, Arunachalam R, Poonguzhali I, Urooj S, Alelyani S. Optimized RNN -based performance prediction of IoT and WSN -oriented smart city application using improved honey badger algorithm. Measurement. 2023 Mar 31; 210:112505

    work page 2023

  8. [8]

    GTxChain: A secure IoT smart blockchain architecture based on graph neural network

    Cai J, Liang W, Li X, Li K, Gui Z, Khan MK. GTxChain: A secure IoT smart blockchain architecture based on graph neural network. IEEE Internet of Things Journal. 2023 Jul 18; 10[24]:21502-14

    work page 2023

  9. [9]

    An improved anomaly detection model for IoT security using decision tree and gradient boosting

    Douiba M, Benkirane S, Guezzaz A, Azrour M. An improved anomaly detection model for IoT security using decision tree and gradient boosting. The Journal of Supercomputing. 2023 Feb; 79[3]:3392-411

    work page 2023

  10. [10]

    Federated learning at the edge in Industrial Internet of Things: A review

    Sah DK, Vahabi M, Fotouhi H. Federated learning at the edge in Industrial Internet of Things: A review. Sustainable Computing: Informatics and Systems. 2025 Jun; 46:101087

    work page 2025

  11. [11]

    Deep learning for cyber threat detection in IoT networks: A review

    Aldhaheri A, Alwahedi F, Ferrag MA, Battah A. Deep learning for cyber threat detection in IoT networks: A review. Internet of Things and cyber-physical systems. 2024 Jan 1; 4:110-28

    work page 2024

  12. [12]

    Using artificial intelligence (ai) and internet of things (iot) for improving network security by hybrid cryptography approach

    Sumathi MS, J Shruthi, Jain V, G Kalyan K, Zarrarahmed ZK. Using artificial intelligence (ai) and internet of things (iot) for improving network security by hybrid cryptography approach. Evergreen. 2023 June; 10[2]:1133-1139

    work page 2023

  13. [13]

    Enhancing IoT Security with Asynchronous Federated Learning for Seismic Inversion

    Manu D, Lin Y, Yao J, Li Z, Sun X. Enhancing IoT Security with Asynchronous Federated Learning for Seismic Inversion. In : 2024 IEEE International Conference on EAI Endorsed Transactions on Internet of Things | Volume 11 | 2025 | K. Ashik et al. 16 Communications Workshops (ICC Workshops) . 2024 Jun 9-13; Denver, Colorado, USA: IEEE, 2024. pp. 1493-1498

    work page 2024

  14. [14]

    BFLIDS: Blockchain-driven federated learning for intrusion detection in IoMT networks

    Begum K, Mozumder MA, Joo MI, Kim HC. BFLIDS: Blockchain-driven federated learning for intrusion detection in IoMT networks. Sensors. 2024 Jul 15; 24[14]:4591

    work page 2024

  15. [15]

    The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation

    Nasiruzzaman M, Ali M, Salam I, Miraz MH. The Evolution of Zero Trust Architecture (ZTA) from Concept to Implementation. In: 2025 29th International Conference on Information Technology (IT) . 2025 Feb 19- 22; Zabljak, Montenegro: IEEE, 2025. pp. 1-8

    work page 2025

  16. [16]

    Decentralized federated graph learning with lightweight zero trust architecture for next -generation networking security

    Zhou X, Liang W, Kevin I, Wang K, Yada K, Yang LT, Ma J, Jin Q. Decentralized federated graph learning with lightweight zero trust architecture for next -generation networking security. IEEE Journal on Selected Areas in Communications. 2025 Apr 15; 43[6]:1908-1922

    work page 2025

  17. [17]

    Zta-iot: A novel architecture for zero -trust in iot systems and an ensuing usage control model

    Ameer S, Praharaj L, Sandhu R, Bhatt S, Gupta M. Zta-iot: A novel architecture for zero -trust in iot systems and an ensuing usage control model. ACM Transactions on Privacy and Security. 2024 Aug 16; 27[3]:1-36

    work page 2024

  18. [18]

    COSIER: A comprehensive lightweight blockchain system for IoT networks

    Mershad K. COSIER: A comprehensive lightweight blockchain system for IoT networks. Computer Communications. 2024 Aug 1; 224:125-44

    work page 2024

  19. [19]

    Federated Learning and Blockchain Framework for Scalable and Secure IoT Access Control

    Odeh A, Taleb AA. Federated Learning and Blockchain Framework for Scalable and Secure IoT Access Control. Computers, Materials & Continua. 2025 Jul 1;84[1]

    work page 2025

  20. [20]

    Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis

    Ferrag MA, Friha O, Maglaras L, Janicke H, Shu L. Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis. IEEe Access. 2021 Oct 6; 9:138509-42

    work page 2021

  21. [21]

    Using machine learning algorithms to enhance IoT system security

    El-Sofany H, El-Seoud SA, Karam OH, Bouallegue B. Using machine learning algorithms to enhance IoT system security. Scientific Reports. 2024 May 27; 14[1]:12077

    work page 2024

  22. [22]

    Multi-objectives reinforcement federated learning blockchain enabled Internet of things and Fog-Cloud infrastructure for transport data

    Mohammed MA, Lakhan A, Abdulkareem KH, Abd Ghani MK, Marhoon HA, Nedoma J, Martinek R. Multi-objectives reinforcement federated learning blockchain enabled Internet of things and Fog-Cloud infrastructure for transport data. Heliyon. 2023 Nov 1; 9[11]

    work page 2023

  23. [23]

    Open problems and challenges in federated learning for IoT: A comprehensive review and strategic guide

    Diba BS, Plabon JD, Mowla TJ, Nahar N, Mistry D, Sarker S, Mridha MF, Shin J. Open problems and challenges in federated learning for IoT: A comprehensive review and strategic guide. Computers and Electrical Engineering. 2025 Aug 1; 126:110515

    work page 2025

  24. [24]

    Assessing IoT intrusion detection computational costs when using a convolutional neural network

    Nicho M, Cusack B, McDermott CD, Girija S. Assessing IoT intrusion detection computational costs when using a convolutional neural network. Information Security Journal: A Global Perspective. 2025 Apr 27; 34[5]:471-491

    work page 2025

  25. [25]

    Cyber risks on IoT platforms and zero trust solutions

    Tanque M, Foxwell HJ. Cyber risks on IoT platforms and zero trust solutions. Advances in Computers . 2023 Jan 1; 131:79-148

    work page 2023

  26. [26]

    Towards a Standard Framework for Blockchain Interoperability: A Position Paper

    Belchior R, Scuri S, Nunes N, Hardjono T, Vasconcelos A. Towards a Standard Framework for Blockchain Interoperability: A Position Paper. In : 2024 IEEE International Conference on Blockchain and Cryptocurrency (ICBC); 2024 June 1-5; Brisbane, Australia: IEEE; 2024. pp.1-5

    work page 2024

  27. [27]

    A survey on IoT trust model frameworks

    Ferraris D, Fernandez-Gago C, Roman R, Lopez J. A survey on IoT trust model frameworks. The Journal of Supercomputing. 2024 Apr; 80[6]:8259-96

    work page 2024

  28. [28]

    A survey of security in zero trust network architectures

    Denzel K. A survey of security in zero trust network architectures. GSC Advanced Research Reviews. 2025 Feb 23; 22[02]:182–214

    work page 2025

  29. [29]

    A survey on blockchain-based trust management for Internet of Things

    Liu Y, Wang J, Yan Z, Wan Z, Jäntti R. A survey on blockchain-based trust management for Internet of Things. IEEE internet of Things Journal. 2023 Jan 18; 10[7]:5898- 922

    work page 2023

  30. [30]

    Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications

    Dritsas E, Trigka M. Federated Learning for IoT: A Survey of Techniques, Challenges, and Applications. Journal of Sensor and Actuator Networks. 2025 Jan 22; 14[1]:9

    work page 2025

  31. [31]

    Exploring the Emerging Technologies Within the Blockchain Landscape

    Tareq MA, Tripathi P, Issa NM, Miraz MH. Exploring the Emerging Technologies Within the Blockchain Landscape . In: Miraz, M.H., Southall, G., Ali, M., Ware, A. (eds) Emerging Technologies in Computing. iCETiC 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2023 Aug 16-18; Southend-on-Sea, ...

    work page 2023

  32. [32]

    Deep trust: A novel framework for dynamic trust and reputation management in the internet of things (iot) - based networks

    Ullah F, Salam A, Amin F, Khan IA, Ahmed J, Zaib SA, Choi GS. Deep trust: A novel framework for dynamic trust and reputation management in the internet of things (iot) - based networks. IEEE Access. 2024 Jun 4; 12:87407-19

    work page 2024

  33. [33]

    NeuroTrust —Artificial-neural -network-based intelligent trust management mechanism for large-scale Internet of Medical Things

    Awan KA, Din IU, Almogren A, Almajed H, Mohiuddin I, Guizani M. NeuroTrust —Artificial-neural -network-based intelligent trust management mechanism for large-scale Internet of Medical Things. IEEE Internet of Things Journal. 2020 Oct 6; 8[21]:15672-82

    work page 2020

  34. [34]

    Firefly algorithm based WSN -IoT security enhancement with machine learning for intrusion detection

    Karthikeyan M, Manimegalai D, RajaGopal K. Firefly algorithm based WSN -IoT security enhancement with machine learning for intrusion detection. Scientific Reports. 2024 Jan 2; 14[1]:231

    work page 2024

  35. [35]

    Enhancing IoT security: a collaborative framework integrating federated learning, dense neural networks, and blockchain

    Nazir A, He J, Zhu N, Anwar MS, Pathan MS. Enhancing IoT security: a collaborative framework integrating federated learning, dense neural networks, and blockchain. Cluster Computing. 2024 Sep; 27[6]:8367-92

    work page 2024

  36. [36]

    Cloud- IIo T-based electronic health record privacy -preserving by CNN and blockchain-enabled federated learning

    Alzubi JA, Alzubi OA, Singh A, Ramachandran M. Cloud- IIo T-based electronic health record privacy -preserving by CNN and blockchain-enabled federated learning. IEEE Transactions on Industrial Informatics. 2022 Jul 7; 19[1]:1080-7

    work page 2022

  37. [37]

    Security of Internet of Things (IoT) using federated learning and deep learning— Recent advancements, issues and prospects

    Gugueoth V, Safavat S, Shetty S. Security of Internet of Things (IoT) using federated learning and deep learning— Recent advancements, issues and prospects. ICT express. 2023 Oct 1; 9[5]:941-60

    work page 2023

  38. [38]

    Integration of blockchain technology and federated learning in vehicular (iot) networks: A comprehensive survey

    Javed AR, Hassan MA, Shahzad F, Ahmed W, Singh S, Baker T, Gadekallu TR. Integration of blockchain technology and federated learning in vehicular (iot) networks: A comprehensive survey. Sensors. 2022 Jun 10; 22[12]:4394

    work page 2022

  39. [39]

    Advancements in securing federated learning with IDS: a comprehensive review of neural networks and feature engineering techniques for malicious client detection

    Latif N, Ma W, Ahmad HB. Advancements in securing federated learning with IDS: a comprehensive review of neural networks and feature engineering techniques for malicious client detection. Artificial Intelligence Review. 2025 Jan 13; 58[3]:91

    work page 2025

  40. [40]

    Blockchained federated learning for internet of things: A comprehensive survey

    Jiang Y, Ma B, Wang X, Yu G, Yu P, Wang Z, Ni W, Liu RP. Blockchained federated learning for internet of things: A comprehensive survey. ACM Computing Surveys. 2024 Jun 22; 56[10]:1-37

    work page 2024

  41. [41]

    Smart deep learning model for enhanced IoT intrusion detection

    Alsubaei FS. Smart deep learning model for enhanced IoT intrusion detection. Scientific Reports. 2025 Jul 1; 15[1]:20577

    work page 2025

  42. [42]

    A context -aware zero trust -based hybrid approach to IoT-based self-driving vehicles security

    Khan IA, Keshk M, Hussain Y, Pi D, Li B, Kousar T, Ali BS. A context -aware zero trust -based hybrid approach to IoT-based self-driving vehicles security. Ad Hoc Networks. 2025 Feb 2; 167:103694

    work page 2025

  43. [43]

    Blockchain and AI-based methods for trust management in IoT: A comprehensive survey

    D’aniello G, Fotia L. Blockchain and AI-based methods for trust management in IoT: A comprehensive survey. Internet of Things. 2025 Sep 9:101755

    work page 2025

  44. [44]

    Enhancing IoT network security through deep learning-powered

    Baksh SA, Khan MA, Ahmed F, Alshehri MS, Ali H, Ahmad J. Enhancing IoT network security through deep learning-powered. Internet of Things. 2023 Dec; 24: p. 100936

    work page 2023

  45. [45]

    Hazman C, Guezzaz A, Benkirane S, Azrour M. A smart model integrating LSTM and XGBoost for improving IoT - EAI Endorsed Transactions on Internet of Things | Volume 11 | 2025 | Decentralised Trust and Enhanced Security in IoT Networks: A Comprehensive Review 17 enabled smart cities security. Cluster Computing. 2025 Feb; 28[1]:70. EAI Endorsed Transactions ...

    work page 2025