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AI-Based Security and Privacy for IoT Applications
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AI-Based Security and Privacy for IoT Applications

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2295

Special Issue Editors

Prof. Dr. Yanjiao Chen

E-Mail Website
Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: IoT security; AI security; spectrum allocation
Dr. Zhenchang Xia

E-Mail Website
Guest Editor
School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
Interests: IoT; internet of vehicles; network congestion control, and reinforcement learning

Special Issue Information

Dear Colleagues,

The rapid development of the Internet of Things (IoT) has advanced the innovation of emerging applications, e.g., smart cities, smart transportations, smart homes, and smart healthcare, spreading over and greatly improving people’s lives at every corner. In turn, with the increasing complexity and volume of generated data in IoT and the growing demand for high-quality customized services from IoT users, IoT is expected to be endowed with more powerful capacities via artificial intelligence (AI) to deal with complicated applications and service requirements, such as automatic sensory data collection, intelligent data fusion, and strategic decision making, in more critical and fine-grained scenarios. Such AI-powered IoT is supposed to be self-intelligence to adapt dynamic environments, interact with humans, and provide smart solutions via the process of sensing, perceiving, learning, and computing. Moreover, the output of AI-powered IoT should be explainable and understandable to humans, for positive interactions, effective guidance, and more predictable and reliable system performance, which is accompanied by unprecedented technical challenges. Although some attempts have been made to develop explainable techniques for AI-powered IoT, this study in both academia and industry is still at the very initial stages. Thus, seeking novel designs and methods to accelerate the development of explainable techniques for AI-powered IoT becomes necessary.

The Special Issue solicits high-quality contributions that focus on the design and development of new technologies, algorithms, and tools to advance explanation and reasoning for AI-powered IoT.

The topics of interest include, but are not limited to:

  • Explainable AI solutions for data collection, management, and analysis in IoT
  • Graph Neural Network/Attention-based solutions for data mining in IoT
  • Explainable AI solutions for decision making in IoT smart applications
  • Explainable incentive mechanism design in collaborative frameworks for sensing/learning/computing in AI-powered IoT
  • Graph embedding-based solutions to graph data mining for IoT big data
  • Access control and authentication
  • Security and privacy of mobile applications
  • Data encryption and signature
  • Explainable AI strategies to defend security threats in IoT
  • Explainable privacy protection methods for IoT data with AI support
  • Network resource management schemes for IoT with AI support
  • AI solutions for communications in IoT
  • AI solutions for fog/edge/cloud services in IoT
  • AI solutions for blockchain deployment in IoT
  • Knowledge-based AI solutions for data processing in IoT
  • Reasoning over data and behaviors in AI-powered IoT devices
  • Analysis and evaluation of design and operations of AI-powered IoT
  • Deep-learning-based explainable AI for protocol design in IoT
  • Traceable distributed learning (e.g., federated learning and split learning) for large-scale AI-powered IoT

Prof. Dr. Yanjiao Chen
Dr. Zhenchang Xia
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

  • network security protection
  • AI-based security
  • privacy protection
  • IoT application
  • blockchain

Published Papers (2 papers)

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Research

18 pages, 2725 KiB  
Article
An Optimization Method for Non-IID Federated Learning Based on Deep Reinforcement Learning
by Xutao Meng, Yong Li, Jianchao Lu and Xianglin Ren
Sensors 2023, 23(22), 9226; https://doi.org/10.3390/s23229226 - 16 Nov 2023
Viewed by 946
Abstract
Federated learning (FL) is a distributed machine learning paradigm that enables a large number of clients to collaboratively train models without sharing data. However, when the private dataset between clients is not independent and identically distributed (non-IID), the local training objective is inconsistent [...] Read more.
Federated learning (FL) is a distributed machine learning paradigm that enables a large number of clients to collaboratively train models without sharing data. However, when the private dataset between clients is not independent and identically distributed (non-IID), the local training objective is inconsistent with the global training objective, which possibly causes the convergence speed of FL to slow down, or even not converge. In this paper, we design a novel FL framework based on deep reinforcement learning (DRL), named FedRLCS. In FedRLCS, we primarily improved the greedy strategy and action space of the double DQN (DDQN) algorithm, enabling the server to select the optimal subset of clients from a non-IID dataset to participate in training, thereby accelerating model convergence and reaching the target accuracy in fewer communication epochs. In simulation experiments, we partition multiple datasets with different strategies to simulate non-IID on local clients. We adopt four models (LeNet-5, MobileNetV2, ResNet-18, ResNet-34) on the four datasets (CIFAR-10, CIFAR-100, NICO, Tiny ImageNet), respectively, and conduct comparative experiments with five state-of-the-art non-IID FL methods. Experimental results show that FedRLCS reduces the number of communication rounds required by 10–70% with the same target accuracy without increasing the computation and storage costs for all clients. Full article
(This article belongs to the Special Issue AI-Based Security and Privacy for IoT Applications)
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23 pages, 1899 KiB  
Article
A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices
by Miyi Zeng, Yue Yao, Hong Liu, Youzhang Hu and Hongyu Yang
Sensors 2023, 23(20), 8576; https://doi.org/10.3390/s23208576 - 19 Oct 2023
Cited by 1 | Viewed by 837
Abstract
To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term ‘modal’ refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes [...] Read more.
To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term ‘modal’ refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode. Full article
(This article belongs to the Special Issue AI-Based Security and Privacy for IoT Applications)
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