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New Challenges to Secure and Intelligent IoT Sensor Systems and...
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New Challenges to Secure and Intelligent IoT Sensor Systems and Applications

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

Deadline for manuscript submissions: closed (25 February 2024) | Viewed by 3179

Special Issue Editors

Dr. Xu Wang

E-Mail Website
Guest Editor
Global Big Data Technologies Centre (GBDTC), University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: cybersecurity and privacy; system and network security; data security and protection
Dr. Ying He

E-Mail Website
Guest Editor
Global Big Data Technologies Centre (GBDTC), University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: signal processing; wireless communication systems and technologies (microwave and millimetre wave)
Prof. Dr. Yingjie Jay Guo

E-Mail Website
Guest Editor
Global Big Data Technologies Centre (GBDTC), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: 5G and 6G antennas; in-band full duplex wireless communications systems; joint communications and sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) sensors have transformed the way we live and work, enabling unprecedented connectivity between the physical and virtual worlds. In addition to challenges in new IoT sensor devices, wireless communication technologies and system architecture, IoT sensor systems and applications are facing new security and privacy challenges. This is partly due to the unique features of IoT systems, including large-scale and heterogeneous networks and resource-limited sensor devices. Consequently, it is imperative to develop security- and privacy-preserving technologies for IoT sensor systems. Intelligence is another prevalent topic in the field of IoT sensors, and research in this field has benefited from rapidly developing artificial intelligence (AI) technologies. Reinforcement learning and neural networks have been developed for complex IoT tasks with limited sensor resources, and federated learning enables IoT systems to exchange knowledge without exposing private data. Moreover, new IoT sensor devices and applications both enable ubiquitous computing and pose further challenges for AI algorithms.

This Special Issue focuses on emerging challenges in secure, privacy-preserving, intelligent and efficient IoT sensor systems and applications, as well as novel solutions empowered by the latest developments in blockchains, federated learning (FL), edge computing, deep learning (DL), neural networks (NNs) and privacy-enhancing technologies (PET). Potential topics include, but are not limited to:

  • New threats to IoT sensor devices, systems and applications;
  • Blockchain-integrated IoT sensor architecture and applications;
  • Privacy-enhancing schemes for IoT applications;
  • Machine learning for IoT security;
  • Secure IoT system design;
  • Efficient IoT system design;
  • Intelligent IoT sensor applications;
  • Prototypes and testbeds for secure and efficient IoT systems and applications.

Dr. Xu Wang
Dr. Ying He
Prof. Dr. Yingjie Jay Guo
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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 and privacy
  • blockchain
  • artificial intelligence
  • federated learning

Published Papers (2 papers)

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Research

17 pages, 669 KiB  
Article
The Threat of Disruptive Jamming to Blockchain-Based Decentralized Federated Learning in Wireless Networks
by Gyungmin Kim and Yonggang Kim
Sensors 2024, 24(2), 535; https://doi.org/10.3390/s24020535 - 15 Jan 2024
Viewed by 710
Abstract
Machine learning techniques have attracted considerable attention for wireless networks because of their impressive performance in complicated scenarios and usefulness in various applications. However, training with and sharing raw data obtained locally from each wireless node does not guarantee privacy and requires a [...] Read more.
Machine learning techniques have attracted considerable attention for wireless networks because of their impressive performance in complicated scenarios and usefulness in various applications. However, training with and sharing raw data obtained locally from each wireless node does not guarantee privacy and requires a large communication overhead. To mitigate such issues, federated learning (FL), in which sharing parameters for model updates are shared instead of raw data, has been developed. FL has also been studied using blockchain techniques to efficiently perform learning in distributed wireless systems without having to deploy a centralized server. Although blockchain-based decentralized federated learning (BDFL) is a promising technique for various wireless sensor networks, malicious attacks can still occur, which result in performance degradation or malfunction. In this study, we analyze the impact of a jamming threats from malicious miners to BDFL in wireless networks. In a wireless BDFL system, it is possible for malicious miners with jamming capability to interfere with the collection of model parameters by normal miners, thus preventing the victim miner from generating a global model. By disrupting normal miners participating in BDFL systems, malicious miners with jamming capability can more easily add malicious data to the mainstream. Through various simulations, we evaluated the success probability performance of malicious block insertion and the participation rate of normal miners in a wireless BDFL system. Full article
(This article belongs to the Special Issue New Challenges to Secure and Intelligent IoT Sensor Systems and Applications)
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20 pages, 4243 KiB  
Article
Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
by Biao Xu, Minyan Lu and Hong Zhang
Sensors 2023, 23(8), 3846; https://doi.org/10.3390/s23083846 - 9 Apr 2023
Cited by 4 | Viewed by 1708
Abstract
Wireless sensor networks (WSNs) are widely used in various fields, and the reliability and performance of WSNs are critical for their applications. However, WSNs are vulnerable to jamming attacks, and the impact of movable jammers on WSNs’ reliability and performance remains largely unexplored. [...] Read more.
Wireless sensor networks (WSNs) are widely used in various fields, and the reliability and performance of WSNs are critical for their applications. However, WSNs are vulnerable to jamming attacks, and the impact of movable jammers on WSNs’ reliability and performance remains largely unexplored. This study aims to investigate the impact of movable jammers on WSNs and propose a comprehensive approach for modeling jammer-affected WSNs, comprising four parts. Firstly, agent-based modeling of sensor nodes, base stations, and jammers has been proposed. Secondly, a jamming-aware routing protocol (JRP) has been proposed to enable sensor nodes to weigh depth and jamming values when selecting relay nodes, thereby bypassing areas affected by jamming. The third and fourth parts involve simulation processes and parameter design for simulations. The simulation results show that the mobility of the jammer significantly affects WSNs’ reliability and performance, and JRP effectively bypasses jammed areas and maintains network connectivity. Furthermore, the number and deployment location of jammers has a significant impact on WSNs’ reliability and performance. These findings provide insights into the design of reliable and efficient WSNs under jamming attacks. Full article
(This article belongs to the Special Issue New Challenges to Secure and Intelligent IoT Sensor Systems and Applications)
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