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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 - 09 Apr 2024

Viewed by 419

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, [...] Read more.

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 - 03 Feb 2024

Viewed by 859

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 [...] Read more.

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 918

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 [...] Read more.

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 1 | Viewed by 1636

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 [...] Read more.

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 - 02 Mar 2023

Cited by 6 | Viewed by 1553

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 [...] Read more.

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 - 03 Jan 2023

Cited by 9 | Viewed by 2103

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 [...] Read more.

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 - 03 Dec 2022

Cited by 3 | Viewed by 904

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 [...] Read more.

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

Jump to: Research

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 3 | Viewed by 2131

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 [...] Read more.

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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