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Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT)

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 17730

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Special Issue Editors

Prof. Dr. Shibo He

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Guest Editor

College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: machine learning; internet of things; industrial big data

Dr. Fangyuan Xing

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Guest Editor

School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
Interests: wireless communications; Internet of Things; network optimization

Prof. Dr. Victor C. M. Leung

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1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
2. Department of Electrical & Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: resource allocation; learning (artificial intelligence)
Special Issues, Collections and Topics in MDPI journals

Dr. Lei Yang

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Guest Editor

Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA
Interests: network

Prof. Dr. Huan Zhou

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Guest Editor

College of Computer Science and Technology, China Three Gorges University, Yichang 443002, China
Interests: mobile crowdsensing; mobile edge computing; VANETs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence of Things (AIoT) is considered a collaborative application of Artificial Intelligence (AI) and the Internet of Things (IoT). With the fast growth of Internet of Things (IoT), vehicular networks, and the advent of 5G/6G, there are more rigorous performance requirements (e.g., ultra-low latency and ultra-high reliability) for advanced communication and networking techniques that enable the emerging high mobility applications.

Future AIoT systems will provide intelligent wireless connections with a high data rate for anyone at anytime and anywhere with the aid of AI, for example when traveling in high-speed trains and highway vehicles. These high mobility scenarios result in rapidly time-varying channels, which pose urgent demands for AI-empowered large-scale communications as well as significant challenges for the design of communication and networking models and technologies for AIoT.

This Special Issue aims to collect original and high-quality submissions that target the relevant theoretical aspects and practical design of advanced communication and networking techniques for high mobility networks. The topics of interest include but are not limited to:

Rapidly time-varying channel modeling, estimation, and equalization
Machine learning-based big data analytics for AIoT systems
Security and privacy in AIoT systems
Doppler shift estimation and compensation
Efficient modulation and detection techniques for highly mobile environments
Highly dynamic radio resource optimization
Multiple access schemes for AIoT
Ultra-high reliability routing protocols
Relay, distributive multi-antenna, and cooperative techniques
Communication and networking for highly mobile underwater IoT
Next generation techniques for highly mobile optical wireless communications

Prof. Dr. Shibo He
Dr. Fangyuan Xing
Prof. Dr. Victor C. M. Leung
Dr. Lei Yang
Prof. Dr. Huan Zhou
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. Electronics 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 2400 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

artificial intelligence
IoT
communication and networking

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Published Papers (12 papers)

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Research

13 pages, 765 KiB

Open AccessArticle

A Federated-Learning Algorithm Based on Client Sampling and Gradient Projection for the Smart Grid

by Ruifeng Zhao, Jiangang Lu, Zewei Liu, Tianqi Wang, Wenxin Guo, Tian Lan and Chunqiang Hu

Electronics 2024, 13(11), 2023; https://doi.org/10.3390/electronics13112023 - 22 May 2024

Viewed by 815

Abstract

Federated learning (FL) is a machine-learning framework that effectively addresses privacy concerns. It harnesses fragmented data from devices across the globe for model training and optimization while strictly adhering to user privacy protection and regulatory compliance. This framework holds immense potential for widespread applications in the smart-grid domain. Through FL, power companies can collaborate to train smart-grid models without revealing users’ electricity consumption data, thus safeguarding their privacy. However, the data collected by clients often exhibits heterogeneity, which can lead to biases towards certain data features during the model-training process, therefore affecting the fairness and performance of the model. To tackle the fairness challenges that emerge during the federated-learning process in smart grids, this paper introduces FedCSGP, a novel federated-learning approach that incorporates client sampling and gradient projection. The main idea of FedCSGP is to categorize the causes of unfairness in federated learning into two parts: internal conflicts and external conflicts. Among them, the client-sampling strategy is used to resolve external conflicts, while the gradient-projection strategy is employed to address internal conflicts. By tackling both aspects, FederCSGP aims to enhance the fairness of the federated-learning model while ensuring the accuracy of the global model. The experimental results demonstrate that the proposed method significantly improves the accuracy of poorly performing clients in smart-grid scenarios with lower communication costs, therefore enhancing the fairness of the federated-learning algorithm. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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16 pages, 472 KiB

Open AccessArticle

Resilient Integrated Control for AIOT Systems under DoS Attacks and Packet Loss

by Xiaoya Cao, Wenting Wang, Zhenya Chen, Xin Wang and Ming Yang

Electronics 2024, 13(9), 1737; https://doi.org/10.3390/electronics13091737 - 1 May 2024

Viewed by 1034

Abstract

This paper addresses bandwidth limitations resulting from Denial-of-Service (DoS) attacks on Artificial Intelligence of Things (AIOT) systems, with a specific focus on adverse network conditions. First, to mitigate the impact of DoS attacks on system bandwidth, a novel model predictive control combined with a dynamic time-varying quantization interval adjustment technique is designed for the encoder–decoder architecture of AIOT systems. Second, the network state is modeled to represent a Markov chain under suboptimal network conditions. Furthermore, to guarantee the stability of AIOT systems under random packet loss, a Kalman filter algorithm is applied to precisely estimate the system state. By leveraging the Lyapunov stability theory, the maximum tolerable probability of random packet loss is determined, thereby enhancing the system’s resilient operation. Simulation results validate the effectiveness of the proposed method in dealing with DoS attacks and adverse network conditions. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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17 pages, 10859 KiB

Open AccessArticle

A Reconfigurable Local Oscillator Harmonic Mixer with Simultaneous Phase Shifting and Image Rejection

by Bin Wu, Chaoyue Zheng, Hao Zhang and Qingchun Zhao

Electronics 2024, 13(5), 971; https://doi.org/10.3390/electronics13050971 - 3 Mar 2024

Viewed by 1115

Abstract

The multibeam high-throughput satellites (HTS) are regarded as a crucial component in the forthcoming space-based Internet of Things (S-IoT) network. The multi-band frequency conversion capability of HTS is essential for achieving high-capacity information interconnection in the S-IoT network. To enhance the frequency conversion capability of the on-board payload, a reconfigurable local oscillator (LO) harmonic mixer with simultaneous phase shifting and image-rejection is proposed and demonstrated based on a polarization division multiplexing dual-parallel Mach–Zehnder modulator (PDM-DPMZM). By adjusting the input radio frequency (RF) signal and direct current (DC) bias voltage of the modulator, four different LO frequency-multiplication mixing functions can be achieved. The phase of the generated signal can be flexibly tuned over a full 360° range by controlling the angle α between the polarization direction of the polarizer and one axis of the modulator, and it has a flat amplitude response. When combined with an optical frequency comb, the scheme can be extended to a multi-channel multi-band frequency conversion system with an independent phase tuning capability. Additionally, by adjusting the phase difference between dual channel output signals, it can be reconfigured to implement in-phase/quadrature (I/Q) mixing, double-balanced mixing and image-reject mixing. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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14 pages, 7448 KiB

Open AccessFeature PaperArticle

Next-Generation IoT: Harnessing AI for Enhanced Localization and Energy Harvesting in Backscatter Communications

by Rory Nesbitt, Syed Tariq Shah, Mahmoud Wagih, Muhammad A. Imran, Qammer H. Abbasi and Shuja Ansari

Electronics 2023, 12(24), 5020; https://doi.org/10.3390/electronics12245020 - 15 Dec 2023

Cited by 3 | Viewed by 1378

Abstract

Ongoing backscatter communications and localisation research have been able to obtain incredibly accurate results in controlled environments. The main issue with these systems is faced in complex RF environments. This paper investigates concurrent localization and ambient radio frequency (RF) energy harvesting using backscatter communication systems for Internet of Things networks. Dynamic real-world environments introduce complexity from multipath reflection and shadowing, as well as interference from movements. A machine learning framework leveraging K-Nearest Neighbors and Random Forest classifiers creates robustness against such variability. Historically, received signal measurements construct a location fingerprint database resilient to perturbations. The Random Forest model demonstrates precise localization across customized benches with programmable shuffling of chairs outfitted with RF identification tags. Average precision accuracy exceeds 99% despite deliberate placement modifications, inducing signal fluctuations emulating mobility and clutter. Significantly, directional antennas can harvest over −3 dBm, while even omnidirectional antennas provide −10 dBm—both suitable for perpetually replenishing low-energy electronics. Consequently, the intelligent backscatter platform localizes unmodified objects to customizable precision while promoting self-sustainability. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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28 pages, 8347 KiB

Open AccessArticle

An Approach to the State Explosion Problem: SOPC Case Study

by Shan Zhou, Jinbo Wang, Panpan Xue, Xiangyang Wang and Lu Kong

Electronics 2023, 12(24), 4987; https://doi.org/10.3390/electronics12244987 - 13 Dec 2023

Cited by 1 | Viewed by 1642

Abstract

The system on a programmable chip (SOPC) architecture is better than traditional central processing unit (CPU) + field-programmable gate array (FPGA) architecture. It forms an efficient coupling between processor software and programmable logic through an on-chip high-speed bus. The SOPC architecture is resource-rich and highly customizable. At the same time, it combines low power consumption and high performance, making it popular in the field of high reliability and other new industrial fields. The SOPC architecture system is complex and integrates multiple forms of intellectual property (IP). Because of this, the traditional dynamic test and the static test cannot meet the requirements for test depth. To solve the problem of verification depth, we should introduce formal verification. But there are some types of IP forms that formal tools cannot recognize. These include black box IP, encrypted IP, and netlist IP in the SOPC model. Also, the state space explosion caused by the huge scale of the SOPC model cannot be formally verified. In this paper, we propose a modeling method using SOPC architecture. The model solves the problem of formal tools not recognizing multi-form IPs. To compress the state space, we propose reducing SOPC variables and branch relationships based on verification properties. Then, we conduct a property verification experiment on the reduced SOPC model. The experiment result shows that the model can significantly reduce the verification time. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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14 pages, 3642 KiB

Open AccessArticle

Coverage Enhancement of Light-Emitting Diode Array in Underwater Internet of Things over Optical Channels

by Anliang Liu, Huiping Yao, Haobo Zhao, Yingming Yuan and Yujia Wang

Electronics 2023, 12(23), 4736; https://doi.org/10.3390/electronics12234736 - 22 Nov 2023

Viewed by 758

Abstract

The construction of the underwater Internet of Things (UIoT) is crucial to marine resource development, environmental observation, and tactical surveillance. The underwater optical wireless communication (UOWC) system with its large bandwidth and wide coverage facilitates the high-capacity information interconnection within the UIoT networks over short and medium ranges. To enhance the coverage characteristics of the UOWC system, an optimized lemniscate-compensated layout of light-emitting diode (LED) array is proposed in this paper, which can ameliorate the received optical power and reliability at the receiving terminal. Compared with traditional circular and rectangular layouts, the received optical power and bit error rate (BER) performance of the proposed system are analyzed based on the Monte Carlo simulation method. The analysis results show that the proposed LED array achieves a smaller peak power deviation and mean square error of the received optical power under three typical seawater environments. Furthermore, the proposed LED-array scheme supports a better BER performance of the UOWC system. For example, in turbid seawater with a transmission depth of 9.5 m, the BER of the proposed LED array layout is 1 × 10⁻⁷, which is better than the BER of 3.5 × 10⁻⁶ and 1 × 10⁻⁴ under the other two traditional light source layouts. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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17 pages, 607 KiB

Open AccessArticle

Survey on Application of Trusted Computing in Industrial Control Systems

by Jing Bai, Xiao Zhang, Longyun Qi, Wei Liu, Xianfei Zhou, Yin Liu, Xiaoliang Lv, Boyan Sun, Binbin Duan, Siyuan Zhang and Xin Che

Electronics 2023, 12(19), 4182; https://doi.org/10.3390/electronics12194182 - 9 Oct 2023

Viewed by 1826

Abstract

The Fourth Industrial Revolution, also known as Industrial 4.0, has greatly accelerated inter-connectivity and smart automation in industrial control systems (ICSs), which has introduced new challenges to their security. With the fast growth of the Internet of Things and the advent of 5G/6G, the collaboration of Artificial Intelligence (Al) and the Internet of Things (loT) in ICSs has also introduced lots of security issues as it highly relies on advanced communication and networking techniques. Frequent ICS security incidents have demonstrated that attackers have the ability to stealthily breach the current system defenses and cause catastrophic effects to ICSs. Thankfully, trusted computing technology, which has been a popular research topic in the field of information security in recent years, offers distinct advantages when applied to ICSs. In this paper, we first analyze the vulnerabilities of ICSs and the limitations of existing protection technologies. Then, we introduce the concept of trusted computing and present a security framework for ICSs based on Trusted Computing 3.0. Finally, we discuss potential future research directions. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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14 pages, 5386 KiB

Open AccessFeature PaperArticle

Human Activity Recognition Based on Continuous-Wave Radar and Bidirectional Gate Recurrent Unit

by Junhao Zhou, Chao Sun, Kyongseok Jang, Shangyi Yang and Youngok Kim

Electronics 2023, 12(19), 4060; https://doi.org/10.3390/electronics12194060 - 27 Sep 2023

Cited by 2 | Viewed by 1385

Abstract

The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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14 pages, 1577 KiB

Open AccessArticle

Unauthorized Access Detection for Network Device Firmware WEB Pages

by Minwei Peng, Qiang Wei, Rongkuan Ma, Yangyang Geng, Yahui Yang, Shichao Zhang and Yali Zhang

Electronics 2023, 12(17), 3674; https://doi.org/10.3390/electronics12173674 - 31 Aug 2023

Cited by 1 | Viewed by 1700

Abstract

WEB technology is utilized for the configuration, interaction, and management of network equipment, which has become ubiquitous in the intelligent industry and consumer electronics field. Unauthorized access on WEB allows unauthorized users to access authorized information, causing security vulnerabilities such as information leakage and command execution. However, commonly used vulnerability detection techniques for WEB unauthorized access face increasing challenges and more efficiently identify potentially sensitive pages. We propose WEBUAD, a WEB Unauthorized Access Detection framework, for the vulnerability detection of WEB service IoT network devices. WEBUAD utilizes the depth-first search algorithm to fully mine available information on device firmware and generate a potential-visit page set as well as a similarity–matching algorithm of machine learning to calculate the similarity of the responses of a web request. Finally, we evaluate WEBUAD on 9 real physical devices from four vendors and 190 device firmware from seven vendors. The result shows that compared with the state-of-the-art tool such as IoTScope, WEBUAD discovered 5007 potentially available pages, of which 658 were accessible and 9 sensitive pages existed, taking 50 s. Furthermore, WEBUAD exposed 13 security-critical vulnerabilities. Our approach can be used to automate the discovery of the WEB unauthorized access vulnerabilities of IoT devices. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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17 pages, 5739 KiB

Open AccessArticle

Intelligent Mesh Cluster Algorithm for Device-Free Localization in Wireless Sensor Networks

by Chao Sun, Junhao Zhou, Kyong-Seok Jang and Youngok Kim

Electronics 2023, 12(16), 3426; https://doi.org/10.3390/electronics12163426 - 13 Aug 2023

Cited by 2 | Viewed by 1304

Abstract

Device-free localization (DFL) is a technology designed to determine the positions of targets without the need for them to carry electronic devices. It achieves this by analyzing the shadowing effects of radio links within wireless sensor networks (WSNs). However, obtaining high precision in DFL often results in increased energy consumption, severe electromagnetic interference, and other challenges that impact positioning accuracy. Most DFL schemes for accurate tracking require substantial memory and computing resources, which make them unsuitable for resource-constrained applications. To address these challenges, we propose an intelligent mesh cluster (IMC) algorithm that achieves accurate tracking by adaptively activating a subset of wireless links. This approach not only reduces electromagnetic interference but also saves energy. The IMC algorithm leverages geometric objects, such as meshes and mesh clusters formed by wireless links, to achieve low computational complexity. By scanning a subset of mesh cluster-related wireless links near the DFL target, the algorithm significantly reduces the computational requirements. The target’s location estimate is determined based on the connection information among the mesh clusters. We conducted numerous simulations to evaluate the performance of the IMC algorithm. The results demonstrate that the IMC algorithm outperforms grid-based and particle filter-based DFL methods, confirming its effectiveness in achieving accurate and efficient localization. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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17 pages, 1303 KiB

Open AccessArticle

Fuzzing Technology Based on Information Theory for Industrial Proprietary Protocol

by Xin Che, Yangyang Geng, Ge Zhang and Mufeng Wang

Electronics 2023, 12(14), 3041; https://doi.org/10.3390/electronics12143041 - 11 Jul 2023

Cited by 2 | Viewed by 1315

Abstract

With the rapid development of the Industrial Internet of Things (IIoT), programmable logic controllers (PLCs) are becoming increasingly intelligent, leading to improved productivity. However, this also brings about a growing number of security vulnerabilities. As a result, efficiently identifying potential security vulnerabilities in PLCs has become a crucial research topic for security researchers. This article proposes a method for fuzzing industrial proprietary protocols to effectively identify security vulnerabilities in PLCs’ proprietary protocols. The aim of this study is to develop a protocol fuzzing approach that can uncover security vulnerabilities in PLCs’ proprietary protocols. To achieve this, the article presents a protocol structure parsing algorithm specifically designed for PLC proprietary protocols, utilizing information theory. Additionally, a fuzzing case generation algorithm based on genetic algorithms is introduced to select test cases that adhere to the format specifications of the proprietary protocol while exhibiting a high degree of mutation. The research methodology consists of several steps. Firstly, the proposed protocol structure parsing algorithm is used to analyze two known industrial protocols, namely Modbus TCP and S7Comm. The parsing results obtained from the algorithm are then compared with the correct results to validate its effectiveness. Next, the protocol structure parsing algorithm is applied to analyze the proprietary protocol formats of two PLC models. Finally, based on the analysis results, the PLCs are subjected to fuzzing. Overall, the proposed protocol fuzzing approach, incorporating the protocol structure parsing algorithm and the fuzzing case generation algorithm, successfully identifies two denial-of-service vulnerabilities in the PLCs’ proprietary protocols. Notably, one of these vulnerabilities is a zero-day vulnerability, indicating that it was previously unknown and undisclosed. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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15 pages, 3697 KiB

Open AccessArticle

IoT Device Identification Method Based on Causal Inference

by Xingkui Wang, Yunhao Wu, Dan Yu, Yuli Yang, Yao Ma and Yongle Chen

Electronics 2023, 12(12), 2727; https://doi.org/10.3390/electronics12122727 - 19 Jun 2023

Cited by 2 | Viewed by 1880

Abstract

With the development of 5G, the number of IoT (Internet of Things) devices connected to the Internet will grow explosively. However, due to the vulnerability of the devices, attackers can launch attacks on the vulnerable IoT devices, causing great impact on the security of the network environment. Fine-grained identification of IoT devices can help network administrators set up appropriate security policies based on the functionality and heterogeneity of the devices, while enabling timely updates and upgrades for devices with security vulnerabilities or the isolation of these dangerous devices. However, most of the existing IoT device identification methods rely on a priori knowledge or expert experience in selecting features, which cannot weigh the identification performance and labor cost. In this paper, we design a fine-grained identification method for IoT devices based on causal inference, which automatically extracts key features in the protocol fields of device communication from the perspective of causality and then classifies key features using a Stacking integrated learning method to achieve high-precision and fine-grained device identification. Through experimental verification, the proposed method achieves 96.3% and 97.7% device model identification accuracy under HTTP/TCP and SSH/TCP protocol clusters. Full article

(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))

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