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AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition

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

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 21994

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Special Issue Editors

Dr. Wei Ni

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Guest Editor

Cybernetics Group, Cyber-Physical System (CPS) Program, CSIRO, Canberra 2601, Australia
Interests: signal processing; communication network; security
Special Issues, Collections and Topics in MDPI journals

Dr. Rajan Shankaran

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Guest Editor

Department of Computing, Macquarie University, Sydney, NSW 2109, Australia
Interests: wireless and mobile networks security; sensor networks security; QoS and energy-aware routing; cognitive radio networks; security in mobile ad hoc networks; denial of service attacks in Internet of Things; trust management in ad hoc/sensor networks; key management in ad hoc/sensor networks
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaojing Chen

E-Mail Website
Guest Editor

Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China
Interests: green communications; edge computing, stochastic network optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Bochun Wu

E-Mail Website
Guest Editor

1. NIC, Informatization Office, Fudan University, Shanghai 200433, China
2. Key Laboratory of EMW Information (MoE), Fudan University, Shanghai 200433, China
Interests: IoT/IoV; HetNet; MEC; UAV; resource allocation; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As part of modern communication technologies, a variety of sensors are deployed to exchange data between the physical and cyber worlds. In the age of 5G, wireless sensor networks (WSNs) promise to overturn human–machine interactive styles in Internet-of-Things (IoT) and Internet-of-Vehicles (IoV) industries. The integrity of WSNs and emerging technologies (e.g., AI, edge computing, unmanned aerial vehicles (UAVs), intelligent buildings, and smart grids) has also enabled new mobile applications such as virtual reality (VR)/augmented reality (AR), holographic telemedicine, autonomous driving, indoor localization, and crowd behavior identification. On the other hand, cyber-physical systems (CPSs) feature tight coordination between computations and controls via networking communications. However, constructing a paradigm to improve cyber-physical coordination in communications, controls, and computations remains unexplored. Detailed studies of WSNs and CPSs will significantly contribute to developing 5G/AI networking technologies.

This Special Issue, entitled “AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition”, is addressed to adopt AI-based schemes to solve wireless communication problems in WSNs and CPSs, and to study all types of AI-based sensing and networking applications.

Topics of interest include, but are not limited to:

Communications, controls, and computations in WSNs and CPSs;
Security in WSNs and CPSs;
Machine-learning-aided WSNs and CPSs;
UAV-aided WSNs and CPSs;
Multi-agent coordination and combinatorial/distributed decisions in CPSs;
Edge/fog/cloud computing in WSNs and CPSs;
Applications of WSNs and CPSs;
Other emerging subjects in WSNs and CPSs.

Dr. Wei Ni
Dr. Rajan Shankaran
Dr. Xiaojing Chen
Dr. Bochun Wu
Guest Editors

Manuscript Submission Information

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Keywords

wireless sensor networks
cyber-physical systems
AI
IoT/IoV
security
UAV
smart grids
intelligent buildings

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Published Papers (9 papers)

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Research

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18 pages, 3735 KiB

Open AccessArticle

Design and Development Considerations of a Cyber Physical Testbed for Operational Technology Research and Education

by Salaheddin Hosseinzadeh, Dionysios Voutos, Darren Barrie, Nsikak Owoh, Moses Ashawa and Alireza Shahrabi

Sensors 2024, 24(12), 3923; https://doi.org/10.3390/s24123923 - 17 Jun 2024

Viewed by 1135

Abstract

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion. Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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23 pages, 4260 KiB

Open AccessArticle

Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN

by Jishan Li, Zhichao Deng, Yong Feng and Nianbo Liu

Sensors 2024, 24(8), 2386; https://doi.org/10.3390/s24082386 - 9 Apr 2024

Cited by 2 | Viewed by 1352

Abstract

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators. Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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20 pages, 1634 KiB

Open AccessArticle

UAV-Based Secure Data Communication: Multilevel Authentication Perspective

by Abdullah Aljumah

Sensors 2024, 24(3), 996; https://doi.org/10.3390/s24030996 - 3 Feb 2024

Cited by 2 | Viewed by 2052

Abstract

An Internet of Things (IoT) system for managing and coordinating unmanned aerial vehicles (UAVs) has revolutionized the industrial sector. The largest issue with the design of the Internet of UAVs (IoUAV) is security. Conspicuously, the novel contribution of the proposed work is to develop a layered authentication approach to facilitate safe IoUAV communication. Specifically, four modules, including the pre-deployment module, user registration module, login module, and authentication module, form the basis of security analysis. In the proposed technique, UAVs are added to the IoUAV registry. The next step is the user registration module, where people are registered with the UAV so they may access the information in real time. In the login module, the user connects with the server for data transmission. Finally, in the authentication module, all entities, including users, servers, and UAVs, are authenticated to ensure secure data communication. The proposed method achieves peak performance as compared to the state-of-the-art techniques in terms of statistical parameters of latency (3.255s), throughput (90.15%), and packet loss (8.854%). Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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15 pages, 1811 KiB

Open AccessArticle

A Graph-Based Technique for Securing the Distributed Cyber-Physical System Infrastructure

by Maxim Kalinin, Evgenii Zavadskii and Alexey Busygin

Sensors 2023, 23(21), 8724; https://doi.org/10.3390/s23218724 - 26 Oct 2023

Viewed by 1432

Abstract

Spreading digitalization, flexibility, and autonomy of technological processes in cyber-physical systems entails high security risks corresponding to negative consequences of the destructive actions of adversaries. The paper proposes a comprehensive technique that represents a distributed functional cyber-physical system’s infrastructure as graphs: a functional dependencies graph and a potential attacks graph. Graph-based representation allows us to provide dynamic detection of the multiple compromised nodes in the functional infrastructure and adapt it to rolling intrusions. The experimental modeling with the proposed technique has demonstrated its effectiveness in the use cases of advanced persistent threats and ransomware. Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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21 pages, 3357 KiB

Open AccessArticle

Dynamic Service Function Chain Deployment and Readjustment Method Based on Deep Reinforcement Learning

by Jing Ran, Wenkai Wang and Hefei Hu

Sensors 2023, 23(6), 3054; https://doi.org/10.3390/s23063054 - 12 Mar 2023

Cited by 3 | Viewed by 2492

Abstract

With the advent of Software Defined Network (SDN) and Network Functions Virtualization (NFV), network operators can offer Service Function Chain (SFC) flexibly to accommodate the diverse network function (NF) requirements of their users. However, deploying SFCs efficiently on the underlying network in response to dynamic SFC requests poses significant challenges and complexities. This paper proposes a dynamic SFC deployment and readjustment method based on deep Q network (DQN) and M Shortest Path Algorithm (MQDR) to address this problem. We develop a model of the dynamic deployment and readjustment of the SFC problem on the basis of the NFV/SFC network to maximize the request acceptance rate. We transform the problem into a Markov Decision Process (MDP) and further apply Reinforcement Learning (RL) to achieve this goal. In our proposed method (MQDR), we employ two agents that dynamically deploy and readjust SFCs collaboratively to enhance the service request acceptance rate. We reduce the action space for dynamic deployment by applying the M Shortest Path Algorithm (MSPA) and decrease the action space for readjustment from two dimensions to one. By reducing the action space, we decrease the training difficulty and improve the actual training effect of our proposed algorithm. The simulation experiments show that MDQR improves the request acceptance rate by approximately 25% compared with the original DQN algorithm and 9.3% compared with the Load Balancing Shortest Path (LBSP) algorithm. Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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15 pages, 6817 KiB

Open AccessArticle

Sensor Data Reconstruction for Dynamic Responses of Structures Using External Feedback of Recurrent Neural Network

by Yoon-Soo Shin and Junhee Kim

Sensors 2023, 23(5), 2737; https://doi.org/10.3390/s23052737 - 2 Mar 2023

Cited by 10 | Viewed by 2300

Abstract

An event of sensor faults in sensor networks deployed in structures might result in the degradation of the structural health monitoring system and lead to difficulties in structural condition assessment. Reconstruction techniques of the data for missing sensor channels were widely adopted to restore a dataset from all sensor channels. In this study, a recurrent neural network (RNN) model combined with external feedback is proposed to enhance the accuracy and effectiveness of sensor data reconstruction for measuring the dynamic responses of structures. The model utilizes spatial correlation rather than spatiotemporal correlation by explicitly feeding the previously reconstructed time series of defective sensor channels back to the input dataset. Because of the nature of spatial correlation, the proposed method generates robust and precise results regardless of the hyperparameters set in the RNN model. To verify the performance of the proposed method, simple RNN, long short-term memory, and gated recurrent unit models were trained using the acceleration datasets obtained from laboratory-scaled three- and six-story shear building frames. Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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38 pages, 2117 KiB

Open AccessArticle

A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing

by Messaoud Babaghayou, Noureddine Chaib, Nasreddine Lagraa, Mohamed Amine Ferrag and Leandros Maglaras

Sensors 2023, 23(1), 531; https://doi.org/10.3390/s23010531 - 3 Jan 2023

Cited by 14 | Viewed by 3034

Abstract

By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary’s exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy “Give up a Pawn to Save a Chariot”. Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use. Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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12 pages, 1667 KiB

Open AccessCommunication

Fixed-Time Synchronization of Coupled Oscillator Networks with a Pacemaker

by Xiufeng Guo, Pengchun Rao and Zhaoyan Wu

Sensors 2022, 22(23), 9460; https://doi.org/10.3390/s22239460 - 3 Dec 2022

Cited by 4 | Viewed by 1274

Abstract

This paper investigates the fixed-time synchronization problem of a Kuramoto–oscillator network in the presence of a pacemaker. Based on the framework of the cyber-physical system (CPS), fixed-time synchronization criteria of such network are presented respectively for identical and non-identical oscillators. In virtue of Lyapunov stability analyses, sufficient conditions are deduced for achieving phase agreement and frequency synchronization for arbitrary initial phases and/or frequencies under distributed control strategies. Theoretical analysis shows that synchronization can be achieved in a fixed time, which is unrelated to initial phases/frequencies. Furthermore, the upper bounds of synchronization time are also obtained. Finally, the numerical simulations also verify the effectiveness of the derived results. Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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Review

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27 pages, 470 KiB

Open AccessReview

A Survey of Latest Wi-Fi Assisted Indoor Positioning on Different Principles

by Jihan Dai, Maoyi Wang, Bochun Wu, Jiajie Shen and Xin Wang

Sensors 2023, 23(18), 7961; https://doi.org/10.3390/s23187961 - 18 Sep 2023

Cited by 17 | Viewed by 5709

Abstract

As the location-based service (LBS) plays an increasingly important role in real life, the topic of positioning attracts more and more attention. Under different environments and principles, researchers have proposed a series of positioning schemes and implemented many positioning systems. With widely deployed networks and massive devices, wireless fidelity (Wi-Fi) technology is promising in the field of indoor positioning. In this paper, we survey the authoritative or latest positioning schemes for Wi-Fi-assisted indoor positioning. To this end, we describe the problem and corresponding applications, as well as an overview of the alternative methods. Then, we classify and analyze Wi-Fi-assisted indoor positioning schemes in detail, as well as review related work. Furthermore, we point out open challenges and forecast promising directions for future work. Full article

(This article belongs to the Special Issue AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems—2nd Edition)

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