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Secure IoT: Cryptographic Solutions for Sensor Networks

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 308

Special Issue Editor


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Guest Editor
School of Computing, Eastern Institute of Technology, Napier, New Zealand
Interests: wireless communication; cognitive radio; internet of things; cyber security; sensor network; ransomware

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) ecosystem continues to expand at an unprecedented scale, integrating billions of sensor nodes into critical infrastructures, healthcare systems, industrial automation, transportation, and environmental monitoring. While these sensors deliver real-time intelligence, their pervasive deployment in open and often adversarial environments exposes them to severe security and privacy risks. Traditional cryptographic mechanisms, designed for resource-rich platforms, remain impractical for highly constrained devices with limited computational power, memory, and energy budgets.

To address these challenges, researchers are advancing lightweight and energy-aware cryptographic schemes, post-quantum algorithms, and hardware-assisted primitives that can secure data confidentiality, integrity, and authentication without overwhelming device resources. Furthermore, contemporary paradigms such as blockchain-enabled trust management, federated learning for anomaly detection, and privacy-preserving protocols are reshaping how sensor networks achieve resilience against evolving threats, including ransomware, supply chain attacks, and quantum adversaries.

This Special Issue will bring together state-of-the-art research on cryptographic innovations tailored for IoT sensor networks, with emphasis on efficiency, scalability, interoperability, and long-term security. This directly aligns with the scope of Sensors, which promotes advances in sensor technologies, architectures, and applications that underpin secure and trustworthy intelligent systems.

Dr. Md Akbar Hossain
Guest Editor

Manuscript Submission Information

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Keywords

  • secure IoT
  • sensor networks
  • lightweight cryptography
  • blockchain based trust management
  • privacy preserving protocols
  • energy efficient encryption
  • post quantum security
  • key management in IoT
  • intrusion and anomaly detection
  • federated and edge intelligence for security

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Published Papers (1 paper)

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Research

20 pages, 632 KB  
Article
Machine Learning Enhanced Quantum-Safe Encryption: A Novel Optimisation Framework
by Rizwan Ahmad, Md Akbar Hossain, Tajrian Mollick and Saifur Rahman Sabuj
Sensors 2026, 26(10), 3226; https://doi.org/10.3390/s26103226 (registering DOI) - 20 May 2026
Abstract
The standardisation of post-quantum cryptography (PQC) by NIST marks a critical transition away from classical public-key schemes towards quantum-resistant successors. As machine learning (ML) applications proliferate, the demand for efficient cryptographic primitives intensifies, requiring implementations that are simultaneously quantum-safe and resource-aware. Recent surveys [...] Read more.
The standardisation of post-quantum cryptography (PQC) by NIST marks a critical transition away from classical public-key schemes towards quantum-resistant successors. As machine learning (ML) applications proliferate, the demand for efficient cryptographic primitives intensifies, requiring implementations that are simultaneously quantum-safe and resource-aware. Recent surveys have investigated the interplay between ML and PQC, with particular focus on ML-assisted parameter optimisation, privacy-preserving ML leveraging lattice-based cryptography, and neural-network implementations of quantum-resistant algorithms. Building on these findings, we propose QSafe-ML, a comprehensive four-stage framework that integrates hardware profiling, surrogate modelling via ML, constrained multi-objective optimisation, and continuous security validation to facilitate the tuning of PQC parameters and implementations. The framework targets NIST-standardised lattice-based schemes CRYSTALS-Kyber, CRYSTALS-Dilithium, Falcon, and NTRU across three heterogeneous hardware platforms. Experimental evaluation with n=30 repeated trials demonstrates mean latency reductions of 27.5–41.9% (95% CI ±1.1–1.7 pp), memory savings of 13.3–30.2%, and energy savings of 22.8–38.2% over NIST reference baselines, with all configurations maintaining ≥128-bit post-quantum security. An ablation study confirms that surrogate-guided search accounts for the dominant share of these gains. All code, data, and benchmark instructions are released at a public repository (available upon acceptance of this manuscript) to promote reproducibility in evaluating ML-assisted cryptographic systems. Full article
(This article belongs to the Special Issue Secure IoT: Cryptographic Solutions for Sensor Networks)
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