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Security Issues and Solutions for the Internet of Things

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

Deadline for manuscript submissions: 25 November 2025 | Viewed by 88

Special Issue Editors


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Guest Editor
Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi" Viale del Risorgimento 2, Bologna, Italy
Interests: sensors; electrical impedance spectroscopy; optical spectroscopy; food analysis; portable sensor systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi" Viale del Risorgimento 2, Bologna, Italy
Interests: fault tolerance; reliability; DfT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of distributed wireless sensor networks has dramatically increased in recent years and boosted the development of the Internet of things (IoT) paradigm. Such sensor networks share a huge amount of information among sensors, as well as between sensors and servers in the cloud. This allows for the collection and sharing of a large amount of information, but also presents security issues. As is known, sensor networks can be particularly susceptible to malicious attacks that threaten system security. In particular, malicious users can launch attacks against a sensor network, thus deteriorating its integrity, confidentiality, and/or availability.

As a consequence, extensive research activity has been/is being carried out to develop proper techniques with which to improve the security of sensor networks, either for the detection of cyberattacks or to mitigate them. Examples of such techniques are lightweight authentication schemes and cryptographic algorithms for resource-constrained devices (like wireless sensor nodes), the use of physical unclonable function (PUF) devices for the generation of safe passwords, the integration of lightweight firewalls to monitor network traffic, and the use of machine learning algorithms to detect the presence of malicious attacks.

The editors welcome submissions of high-quality research papers (not previously published in other journals) presenting innovative strategies with which to mitigate the impact of cyberattacks on wireless sensor networks, as well as review articles discussing recent advancements in the development of strategies to mitigate the impact of such cyberattacks.

Dr. Marco Grossi
Dr. Martin Eugenio Omana
Guest Editors

Manuscript Submission Information

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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

  • Internet of Things
  • security
  • cyberattacks
  • artificial intelligence
  • machine learning
  • authentication
  • cryptography
  • firewall
  • physical unclonable functions
  • denial of service attack

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Published Papers (1 paper)

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Research

43 pages, 5651 KiB  
Article
Cross-Layer Analysis of Machine Learning Models for Secure and Energy-Efficient IoT Networks
by Rashid Mustafa, Nurul I. Sarkar, Mahsa Mohaghegh, Shahbaz Pervez and Ovesh Vohra
Sensors 2025, 25(12), 3720; https://doi.org/10.3390/s25123720 - 13 Jun 2025
Abstract
The widespread adoption of the Internet of Things (IoT) raises significant concerns regarding security and energy efficiency, particularly for low-resource devices. To address these IoT issues, we propose a cross-layer IoT architecture employing machine learning (ML) models and lightweight cryptography. Our proposed solution [...] Read more.
The widespread adoption of the Internet of Things (IoT) raises significant concerns regarding security and energy efficiency, particularly for low-resource devices. To address these IoT issues, we propose a cross-layer IoT architecture employing machine learning (ML) models and lightweight cryptography. Our proposed solution is based on role-based access control (RBAC), ensuring secure authentication in large-scale IoT deployments while preventing unauthorized access attempts. We integrate layer-specific ML models, such as long short-term memory networks for temporal anomaly detection and decision trees for application-layer validation, along with adaptive speck encryption for the dynamic adjustment of cryptographic overheads. We then introduce a granular RBAC system that incorporates energy-aware policies. The novelty of this work is the proposal of a cross-layer IoT architecture that harmonizes ML-driven security with energy-efficient operations. The performance of the proposed cross-layer system is evaluated by extensive simulations. The results obtained show that the proposed system can reduce false positives up to 32% and enhance system security by preventing unauthorized access up to 95%. We also achieve 30% reduction in power consumption using the proposed lightweight Speck encryption method compared to the traditional advanced encryption standard (AES). By leveraging convolutional neural networks and ML, our approach significantly enhances IoT security and energy efficiency in practical scenarios such as smart cities, homes, and schools. Full article
(This article belongs to the Special Issue Security Issues and Solutions for the Internet of Things)
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