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Advancing Cyber Security in the IoT Era: Distributed Ledger and AI Solutions for Enhancing Security and Privacy

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 1772

Special Issue Editors

Dr. Gordon Russell

E-Mail Website
Guest Editor
School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: cyber-security; networks; software systems
Dr. Sean McKeown

E-Mail Website
Guest Editor Assistant
School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: cyber-security; digital forensics

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is rapidly transforming industries and daily life, but its pervasive connectivity also introduces significant cybersecurity vulnerabilities. Traditional security mechanisms often struggle to address the unique challenges posed by the diverse and resource-constrained nature of IoT devices. This Special Issue explores the transformative potential of blockchain and artificial intelligence (AI) in bolstering IoT security and privacy. Blockchain’s decentralized and immutable nature offers robust solutions for secure data management, identity management, and access control. Concurrently, AI’s capabilities in anomaly detection, threat prediction, and intelligent security management provide proactive defense mechanisms against evolving cyber threats. This Special Issue invites original research contributions exploring novel applications, architectures, and theoretical frameworks that leverage the synergy of blockchain and AI to enhance security and privacy in the IoT ecosystem, addressing critical challenges across various IoT domains, from smart cities and industrial IoT to healthcare and beyond.

Dr. Gordon Russell
Guest Editor

Dr. Sean McKeown
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IoT
  • cyber security
  • blockchain and artificial intelligence (AI) in bolstering IoT security and privacy

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

29 pages, 3212 KB  
Article
Secure Hierarchical Asynchronous Federated Learning with Shuffle Model and Mask–DP
by Yonghui Chen, Daxiang Ai and Linglong Yan
Sensors 2026, 26(2), 617; https://doi.org/10.3390/s26020617 - 16 Jan 2026
Viewed by 492
Abstract
Hierarchical asynchronous federated learning (HAFL) accommodates more real networking and ensures practical communications and efficient aggregations. However, existing HAFL schemes still face challenges in balancing privacy-preserving and robustness. Malicious training nodes may infer the privacy of other training nodes or poison the global [...] Read more.
Hierarchical asynchronous federated learning (HAFL) accommodates more real networking and ensures practical communications and efficient aggregations. However, existing HAFL schemes still face challenges in balancing privacy-preserving and robustness. Malicious training nodes may infer the privacy of other training nodes or poison the global model, thereby damaging the system’s robustness. To address these issues, we propose a secure hierarchical asynchronous federated learning (SHAFL) framework. SHAFL organizes training nodes into multiple groups based on their respective gateways. Within each group, the training nodes prevent inference attacks from the gateways and committee nodes via a mask–DP exchange protocol and employ homomorphic encryption (HE) to prevent collusion attacks from other training nodes. Compared with conventional solutions, SHAFL uses noise that can be eliminated to reduce the impact of noise on the global model’s performance, while employing a shuffle model and subsampling to enhance the local model’s privacy-preserving level. At global model aggregation, SHAFL considers both model accuracy and communication delay, effectively reducing the impact of malicious and stale models on system performance. Theoretical analysis and experimental evaluations demonstrate that SHAFL outperforms state-of-the-art solutions in terms of convergence, security, robustness, and privacy-preserving capabilities. Full article
(This article belongs to the Special Issue Advancing Cyber Security in the IoT Era: Distributed Ledger and AI Solutions for Enhancing Security and Privacy)
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18 pages, 916 KB  
Article
SelectVote Byzantine Fault Tolerance for Evidence Custody: Virtual Voting Consensus with Environmental Compensation
by Belinda I. Onyeashie, Petra Leimich, Sean McKeown and Gordon Russell
Sensors 2025, 25(22), 6846; https://doi.org/10.3390/s25226846 - 8 Nov 2025
Viewed by 954
Abstract
Digital evidence custody requires consensus protocols that guarantee immediate and deterministic finality. Legal admissibility depends on proof that no party can alter or delay confirmation of evidence transfers. Conventional Byzantine fault tolerance protocols scale poorly because of quadratic communication overhead, while probabilistic ledger [...] Read more.
Digital evidence custody requires consensus protocols that guarantee immediate and deterministic finality. Legal admissibility depends on proof that no party can alter or delay confirmation of evidence transfers. Conventional Byzantine fault tolerance protocols scale poorly because of quadratic communication overhead, while probabilistic ledger systems such as IOTA and SPECTRE produce confirmation uncertainty that weakens custody verification. This paper introduces SelectVote Byzantine Fault Tolerance, a deterministic consensus protocol that infers virtual votes from graph structure instead of exchanging explicit messages. The protocol operates in permissioned forensic networks and assigns validation witnesses through a fixed, hash-based selection process. Empirical evaluation demonstrates sub-quadratic communication scaling (O(n1.7)) compared to traditional O(n2) Byzantine protocols and maintains Byzantine resilience. To ensure physical integrity, the paper also presents an environmental compensation framework for precision weight verification. The framework models temperature, humidity, and pressure effects on load cells and corrects measurement drift to preserve sub-gram accuracy across normal storage conditions. Experimental evaluation confirms that the integrated system sustains high throughput with deterministic finality and maintains consistent measurement precision under environmental variation. The combined result supports reliable, legally defensible custody of digital evidence across distributed institutions. Full article
(This article belongs to the Special Issue Advancing Cyber Security in the IoT Era: Distributed Ledger and AI Solutions for Enhancing Security and Privacy)
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