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AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems

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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 (31 July 2022) | Viewed by 38843

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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 WSN and emerging technologies, e.g., AI, edge computing, unmanned aerial vehicles (UAV), 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 physical–cyber 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”, is addressed to adopt AI-based schemes to solve wireless communication problems in WSN and CPS 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 WSN and CPS;
Security in WSN and CPS;
Machine learning-aided WSN and CPS;
UAV-aided WSN and CPS;
Multi-agent coordination and combinatorial/distributed decisions in CPS
Edge/fog/cloud computing in WSN and CPS;
Applications of WSN and CPS;
Other emerging subjects of WSN and CPS

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

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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 (12 papers)

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12 pages, 1218 KiB

Open AccessArticle

CPACK: An Intelligent Cyber-Physical Access Control Kit for Protecting Network

by Haisheng Yu, Zhixian Liu, Sai Zou and Wenyong Wang

Sensors 2022, 22(20), 8014; https://doi.org/10.3390/s22208014 - 20 Oct 2022

Viewed by 1574

Abstract

Access Control Lists (ACL) are critical to protecting network and cyber-physical systems. Traditional firewalls mostly use reactive methods to enforce ACLs, so that new ACL updates cannot take effect immediately. In this paper, based on our previous work, we propose CPACK, an intelligent cyber-physical access control kit, which uses a smart algorithm to upgrade the ACL list. CPACK adopts a proactive way to enforce ACL and reacts to a new ACL update and network view update in real time. We implement CPACK on both Floodlight and ONOS controller. We then conduct a large number of experiments to compare CPACK with the Floodlight firewall application. The experimental results show that CPACK has a better performance than the existing Floodlight firewall application. CPACK is also integrated into the new version of Floodlight and ONOS controller. Full article

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

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26 pages, 1233 KiB

Open AccessArticle

Offset-FA: A Uniform Method to Handle Both Unbounded and Bounded Repetitions in Regular Expression Matching

by Chengcheng Xu, Kun Yu, Xinghua Xu, Xianqiang Bao, Songbing Wu and Baokang Zhao

Sensors 2022, 22(20), 7781; https://doi.org/10.3390/s22207781 - 13 Oct 2022

Cited by 1 | Viewed by 1514

Abstract

With the exponential growth of cyber–physical systems (CPSs), security challenges have emerged; attacks on critical infrastructure could result in catastrophic consequences. Intrusion detection is the foundation for CPS security protection, and deep-packet inspection is the primary method for signature-matched mechanisms. This method usually employs regular expression matching (REM) to detect possible threats in the packet payload. State explosion is the critical challenge for REM applications, which originates primarily from features of large character sets with unbounded (closures) or bounded (counting) repetitions. In this work, we propose Offset-FA to handle these repetitions in a uniform mechanism. Offset-FA eliminates state explosion by extracting the repetitions from the nonexplosive string fragments. Then, these fragments are compiled into a fragment-DFA, while a fragment relation table and a reset table are constructed to preserve their connection and offset relationship. To our knowledge, Offset-FA is the first automaton to handle these two kinds of repetitions together with a uniform mechanism. Experiments demonstrate that Offset-FA outperforms state-of-the-art solutions in both space cost and matching speed on the premise of matching correctness, and achieves a comparable matching speed with that of DFA on practical rule sets. Full article

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

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15 pages, 678 KiB

Open AccessArticle

Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater

by Xingkang Tian, Fan Wu, Cong Zhang, Wenhao Fan and Yuanan Liu

Sensors 2022, 22(19), 7257; https://doi.org/10.3390/s22197257 - 24 Sep 2022

Viewed by 1554

Abstract

The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell. Full article

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

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19 pages, 4977 KiB

Open AccessArticle

MEEMD Decomposition–Prediction–Reconstruction Model of Precipitation Time Series

by Yongtao Wang, Jian Liu, Rong Li, Xinyu Suo and Enhui Lu

Sensors 2022, 22(17), 6415; https://doi.org/10.3390/s22176415 - 25 Aug 2022

Cited by 3 | Viewed by 1961

Abstract

To address the problem of low prediction accuracy of precipitation time series data, an improved overall mean empirical modal decomposition–prediction–reconstruction model (MDPRM) is constructed in this paper. First, the non-stationary precipitation time series are decomposed into multiple decomposition terms by the improved overall mean empirical modal decomposition (MEEMD). Then, a particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN) and recurrent neural network (RNN) models are used to make predictions according to the characteristics of different decomposition terms. Finally, the prediction results of each decomposition term are superimposed and reconstructed to form the final prediction results. In addition, the application is carried out with the summer precipitation in the Wujiang River basin of Guizhou Province from 1961 to 2018, using the first 38 years of data to train MDPRM and the last 20 years of data to test MDPRM, and comparing with a feedback neural network (BP), a support vector machine (SVM), a particle swarm optimization support vector machine (PSO-SVM), a convolutional neural network (CNN), and a recurrent neural network (RNN), etc. The results show that the mean relative error (MAPE) of the proposed MDPRM is reduced from 0.31 to 0.09, the root mean square error (RMSE) is reduced from 0.56 to 0.30, and the consistency index (α) is significantly improved from 0.33 to 0.86, which has a higher prediction accuracy. Finally, the trained MDPRM predicts the average summer precipitation in the Wujiang River basin from 2019 to 2028 to be 466.42 mm, the minimum precipitation in 2020 to be 440.94 mm, and the maximum precipitation in 2024 to be 497.94 mm. Based on the prediction results, the agricultural drought level is evaluated using the Z index, which indicates that the summer is normal in the 10-year period. The study provides technical support for the effective guidance of regional water resources’ allocation and scheduling and drought mitigation. Full article

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

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14 pages, 7789 KiB

Open AccessArticle

Attitude Solving Algorithm and FPGA Implementation of Four-Rotor UAV Based on Improved Mahony Complementary Filter

by Yanping Zhu, Jing Liu, Ran Yu, Zijian Mu, Lei Huang, Jinli Chen and Jianan Chen

Sensors 2022, 22(17), 6411; https://doi.org/10.3390/s22176411 - 25 Aug 2022

Cited by 13 | Viewed by 2216

Abstract

With the development of modern industry, small UAVs have been widely used in agriculture, mapping, meteorology, and other fields. There is an increasing demand for the core attitude-solving algorithm of UAV flight control. In this paper, at first, a novel attitude solving algorithm is proposed by using quaternions to represent the attitude matrix and using Allan variance to analyze the gyroscope error and to quantify the trend of the error over time, so as to improve the traditional Mahony complementary filtering. Simulation results show that the six-axis data from the initial sensors (gyroscope and accelerometer) agree well with the measured nine-axis data with an extra magnetometer, which reduces the complexity of the system hardware. Second, based on the hardware platform, the six-axis data collected from MPU6050 are sent to FPGA for floating-point operation, transcendental function operation, and attitude solution module for processing through IIC communication, which effectively validates the attitude solution by using the proposed method. Finally, the proposed algorithm is applied to a practical scenario of a quadrotor UAV, and the test results show that the RMSE does not exceed 2° compared with the extended Kalman filter method. The proposed system simplifies the hardware but keeps the accuracy and speed of the solution, which may result in application in UAV flight control. Full article

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

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16 pages, 1870 KiB

Open AccessArticle

A Mean-Field Game Control for Large-Scale Swarm Formation Flight in Dense Environments

by Guofang Wang, Wang Yao, Xiao Zhang and Ziming Li

Sensors 2022, 22(14), 5437; https://doi.org/10.3390/s22145437 - 21 Jul 2022

Cited by 6 | Viewed by 2663

Abstract

As an important part of cyberphysical systems (CPSs), multiple aerial drone systems are widely used in various scenarios, and research scenarios are becoming increasingly complex. However, planning strategies for the formation flying of aerial swarms in dense environments typically lack the capability of large-scale breakthrough because the amount of communication and computation required for swarm control grows exponentially with scale. To address this deficiency, we present a mean-field game (MFG) control-based method that ensures collision-free trajectory generation for the formation flight of a large-scale swarm. In this paper, two types of differentiable mean-field terms are proposed to quantify the overall similarity distance between large-scale 3-D formations and the potential energy value of dense 3-D obstacles, respectively. We then formulate these two terms into a mean-field game control framework, which minimizes energy cost, formation similarity error, and collision penalty under the dynamical constraints, so as to achieve spatiotemporal planning for the desired trajectory. The classical task of a distributed large-scale aerial swarm system is simulated by numerical examples, and the feasibility and effectiveness of our method are verified and analyzed. The comparison with baseline methods shows the advanced nature of our method. Full article

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

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19 pages, 1930 KiB

Open AccessArticle

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location

by Yunbing Hu, Ao Peng, Biyu Tang, Guojian Ou and Xianzhi Lu

Sensors 2022, 22(14), 5364; https://doi.org/10.3390/s22145364 - 18 Jul 2022

Cited by 4 | Viewed by 1889

Abstract

With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment. Full article

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

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22 pages, 545 KiB

Open AccessArticle

Multi-UAV Content Caching Strategy and Cooperative, Complementary Content Transmission Based on Coalition Formation Game

by Yanzan Sun, Xinlin Zhong, Fan Wu, Xiaojing Chen, Shunqing Zhang and Nan Dong

Sensors 2022, 22(9), 3123; https://doi.org/10.3390/s22093123 - 19 Apr 2022

Cited by 7 | Viewed by 2167

Abstract

The transmission of a large amount of video and picture content brings more challenges to wireless communication networks. Unmanned aerial vehicle (UAV)-aided small cells with active content caching deployed on cellular networks are recognized as a promising way to alleviate wireless backhaul and support flexible coverage. However, a UAV cannot operate for a long time due to limited battery life, and its caching capacity is also limited. For this, a multi-UAV content-caching strategy and cooperative, complementary content transmission among UAVs are jointly studied in this paper. Firstly, a user-clustering-based caching strategy is designed, where user clustering is based on user similarity, concurrently taking into consideration similarities in content preference and location. Then, cooperative, complementary content transmission between multiple UAVs is modeled as a coalition formation game (CFG) to maximize the utility of the whole network. Finally, the effectiveness of the proposed algorithms is demonstrated through numerical simulations. Full article

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

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18 pages, 2384 KiB

Open AccessArticle

Robust Sliding Mode Control for Stochastic Uncertain Discrete Systems with Two-Channel Packet Dropouts and Time-Varying Delays

by Sian Sun, Wenxia Cui and Jie Zheng

Sensors 2022, 22(5), 1965; https://doi.org/10.3390/s22051965 - 2 Mar 2022

Cited by 1 | Viewed by 2225

Abstract

In this paper, the control problem is investigated for discrete time-varying delayed systems with stochastic uncertainty, external disturbance, and two-channel packet dropouts. Sliding mode functions with packet loss probabilities are proposed for the packet loss problem in the sensor–controller channel and the controller–actuator channel. Furthermore, by employing the Lyapunov–Krasovskii functional, some new stability conditions are established in terms of solvable linear matrix inequalities (LMIs), and H_∞ performance is analyzed for the sliding mode motion of the system. Meanwhile, a sliding mode controller is designed to drive the system state to the pre-designed sliding surface. Moreover, the designed controller can be robust for two-channel packet dropouts, time-varying delays, stochastic uncertainty and external disturbance. Finally, two numerical examples are given to demonstrate the feasibility of the proposed theoretical method. Full article

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

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16 pages, 4966 KiB

Open AccessArticle

Cloud-Based User Behavior Emulation Approach for Space-Ground Integrated Networks

by Leiting Tao, Xiaofeng Wang, Yuan Liu and Jie Wu

Sensors 2022, 22(1), 44; https://doi.org/10.3390/s22010044 - 22 Dec 2021

Viewed by 2650

Abstract

Cyber-physical systems (CPSs) based on space-ground integrated networks (SGINs) enable CPSs to break through geographical restrictions in space. Therefore, providing a test platform is necessary for new technical verification and network security strategy evaluations of SGINs. User behavior emulation technology can effectively support the construction of a test platform. Given the inherent dynamic changes, diverse behaviors, and large-scale characteristics of SGIN users, we propose user behavior emulation technology based on a cloud platform. First, the dynamic emulation architecture for user behavior for SGINs is designed. Then, normal user behavior emulation strategy driven by the group user behavior model in real time is proposed, which can improve the fidelity of emulation. Moreover, rogue user behavior emulation technology is adopted, based on traffic replay, to perform the security evaluation. Specifically, virtual Internet Protocol (IP) technology and the epoll model are effectively integrated in this investigation to resolve the contradiction between large-scale emulation and computational overhead. The experimental results demonstrate that the strategy meets the requirement of a diverse and high-fidelity dynamic user behavior emulation and reaches the emulation scale of 100,000-level concurrent communication for normal users and 100,000-level concurrent attacks for rogue users. Full article

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

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Review

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22 pages, 778 KiB

Open AccessReview

Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review

by Ala’ Khalifeh, Felix Mazunga, Action Nechibvute and Benny Munyaradzi Nyambo

Sensors 2022, 22(22), 8937; https://doi.org/10.3390/s22228937 - 18 Nov 2022

Cited by 23 | Viewed by 6768

Abstract

In this paper, a detailed review of microcontroller unit (MCU)-based wireless sensor node platforms from recently published research articles is presented. Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure. Thus, a comparison of the existing wireless sensor nodes is crucial. Of particular interest are the advances in the recent MCU-based wireless sensor node platforms, which have become diverse and fairly advanced in relation to the currently available commercial WSN platforms. The recent wireless sensor nodes are compared with commercially available motes. The commercially available motes are selected based on a number of criteria including popularity, published results, interesting characteristics and features. Of particular interest is to understand the trajectory of development of these devices and the technologies so as to inform the research and application directions. The comparison is mainly based on processing and memory specifications, communication capabilities, power supply and consumption, sensor support, potential applications, node programming and hardware security. This paper attempts to provide a clear picture of the progress being made towards the design of autonomous wireless sensor nodes to avoid redundancy in research by industry and academia. This paper is expected to assist developers of wireless sensor nodes to produce improved designs that outperform the existing motes. Besides, this paper will guide researchers and potential users to easily make the best choice of a mote that best suits their specific application scenarios. A discussion on the wireless sensor node platforms is provided, and challenges and future research directions are also outlined. Full article

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

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20 pages, 857 KiB

Open AccessReview

A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries

by Siguo Bi, Cong Wang, Jilong Zhang, Wutao Huang, Bochun Wu, Yi Gong and Wei Ni

Sensors 2022, 22(8), 2991; https://doi.org/10.3390/s22082991 - 13 Apr 2022

Cited by 38 | Viewed by 9158

Abstract

With the boom in artificial intelligence (AI) and Internet-of-Things (IoT), thousands of smart devices are interconnected with each other and deeply applied into human society. This prosperity has significantly improved public service and management, which were traditionally based on manual work. As a notable scenario, librarianship has embraced an era of “Smart Libraries” enabled by AI and IoT. Unlike existing surveys, our work comprehensively overviews the AI- and IoT-based technologies in three fundamental aspects: smart service, smart sustainability, and smart security. We then further highlight the trend towards future smart libraries. Full article

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

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