Pub Date : 2024-09-09DOI: 10.1109/JSYST.2024.3445377
Fei Zhang;Xingling Shao;Wendong Zhang
This article studies a global positioning system (GPS)-free distributed localization problem for a nonstationary target using a cluster of unmanned aerial vehicles (UAVs) loaded with bearing sensors, which aims to cooperatively estimate the relative positions of target by local interactions, regardless of whether or not the target can be directly detected. First, for leader UAVs that can readily detect the target, a novel bearing-based estimator devised in a local frame is proposed by following a prediction and correction configuration, while a sufficient condition is established to assure the asymptotic decaying of position estimation error. Second, considering follower UAVs that cannot directly observe the target, a special consensus-based cooperative fusion algorithm comprised of coupled observation and localization subsystems is proposed for UAVs to synchronize the target estimation with neighbors’ localization, wherein a fixed-time distributed observer is delicately constructed to provide target speed estimates, such that the requirements on the global availability of target speed can be avoided. The remarkable merit is that without resorting to GPS, all members can reach an agreement on relative positioning estimates in a distributed execution sense. Lyapunov approach certifies that all errors can exponentially approximate to the origin. Simulations confirm the efficacy of the presented algorithm.
{"title":"Cooperative Fusion Localization of a Nonstationary Target for Multiple UAVs Without GPS","authors":"Fei Zhang;Xingling Shao;Wendong Zhang","doi":"10.1109/JSYST.2024.3445377","DOIUrl":"10.1109/JSYST.2024.3445377","url":null,"abstract":"This article studies a global positioning system (GPS)-free distributed localization problem for a nonstationary target using a cluster of unmanned aerial vehicles (UAVs) loaded with bearing sensors, which aims to cooperatively estimate the relative positions of target by local interactions, regardless of whether or not the target can be directly detected. First, for leader UAVs that can readily detect the target, a novel bearing-based estimator devised in a local frame is proposed by following a prediction and correction configuration, while a sufficient condition is established to assure the asymptotic decaying of position estimation error. Second, considering follower UAVs that cannot directly observe the target, a special consensus-based cooperative fusion algorithm comprised of coupled observation and localization subsystems is proposed for UAVs to synchronize the target estimation with neighbors’ localization, wherein a fixed-time distributed observer is delicately constructed to provide target speed estimates, such that the requirements on the global availability of target speed can be avoided. The remarkable merit is that without resorting to GPS, all members can reach an agreement on relative positioning estimates in a distributed execution sense. Lyapunov approach certifies that all errors can exponentially approximate to the origin. Simulations confirm the efficacy of the presented algorithm.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1951-1962"},"PeriodicalIF":4.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1109/JSYST.2024.3450883
Wenjiang Ouyang;Qian Liu;Junsheng Mu;Anwer AI-Dulaimi;Xiaojun Jing;Qilie Liu
Integrated sensing and communication (ISAC) has attracted great attention with the gains of spectrum efficiency and deployment costs through the coexistence of sensing and communication functions. Meanwhile, federated learning (FL) has great potential to apply to large-scale multiagent systems (LSMAS) in ISAC due to the attractive privacy protection mechanism. Nonindependent identically distribution (non-IID) is a fundamental challenge in FL and seriously affects the convergence performance. To deal with the non-IID issue in FL, a data augmentation optimization algorithm (DAOA) is proposed based on reinforcement learning (RL), where an augmented dataset is generated based on a generative adversarial network (GAN) and the local model parameters are inputted into a deep Q-network (DQN) to learn the optimal number of augmented data. Different from the existing works that only optimize the training performance, the number of augmented data is also considered to improve the sample efficiency in the article. In addition, to alleviate the high-dimensional input challenge in DQN and reduce the communication overhead in FL, a lightweight model is applied to the client based on deep separable convolution (DSC). Simulation results indicate that our proposed DAOA algorithm acquires considerable performance with significantly fewer augmented data, and the communication overhead is reduced greatly compared with benchmark algorithms.
{"title":"Communication-Efficient Federated Learning for Large-Scale Multiagent Systems in ISAC: Data Augmentation With Reinforcement Learning","authors":"Wenjiang Ouyang;Qian Liu;Junsheng Mu;Anwer AI-Dulaimi;Xiaojun Jing;Qilie Liu","doi":"10.1109/JSYST.2024.3450883","DOIUrl":"10.1109/JSYST.2024.3450883","url":null,"abstract":"Integrated sensing and communication (ISAC) has attracted great attention with the gains of spectrum efficiency and deployment costs through the coexistence of sensing and communication functions. Meanwhile, federated learning (FL) has great potential to apply to large-scale multiagent systems (LSMAS) in ISAC due to the attractive privacy protection mechanism. Nonindependent identically distribution (non-IID) is a fundamental challenge in FL and seriously affects the convergence performance. To deal with the non-IID issue in FL, a data augmentation optimization algorithm (DAOA) is proposed based on reinforcement learning (RL), where an augmented dataset is generated based on a generative adversarial network (GAN) and the local model parameters are inputted into a deep Q-network (DQN) to learn the optimal number of augmented data. Different from the existing works that only optimize the training performance, the number of augmented data is also considered to improve the sample efficiency in the article. In addition, to alleviate the high-dimensional input challenge in DQN and reduce the communication overhead in FL, a lightweight model is applied to the client based on deep separable convolution (DSC). Simulation results indicate that our proposed DAOA algorithm acquires considerable performance with significantly fewer augmented data, and the communication overhead is reduced greatly compared with benchmark algorithms.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1893-1904"},"PeriodicalIF":4.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/JSYST.2024.3423012
Huifang Li;Jing Li;Meng Liu;Fengkui Gong
In this article, we investigate the physical layer security for a relay-aided multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) system, where an eavesdropper tries to intercept confidential information transmission from the source and the relay by employing selection combining and maximal ratio combining, respectively. Specifically, we propose an optimal transmit antenna selection scheme to exploit the inherent spatial diversity gain for security enhancement. The closed-form expressions for the secrecy outage probability are derived to facilitate the system performance evaluation. At a more pragmatic level, we consider multiple users in the relay-aided MISO NOMA system and thus propose a user pairing algorithm to perfect successive interference cancellation. The algorithm avoids full search over all users by exploiting two-sided matching and low-complexity greed, thereby reducing the total complexity. Furthermore, aiming to maximize the secrecy rate, we formulate an optimization problem. Hence, the power allocation schemes are developed by jointly considering power limits and rate requirements. The scheme achieves closed-form solutions of power allocation for the data rate requirements of each user. Finally, simulation results validate the accuracy of the derived analysis and the improvement significant in secrecy performance by the proposed algorithm and scheme.
{"title":"Performance Analysis and Secure Resource Allocation for Relay-Aided MISO-NOMA Systems","authors":"Huifang Li;Jing Li;Meng Liu;Fengkui Gong","doi":"10.1109/JSYST.2024.3423012","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3423012","url":null,"abstract":"In this article, we investigate the physical layer security for a relay-aided multiple-input single-output (MISO) nonorthogonal multiple access (NOMA) system, where an eavesdropper tries to intercept confidential information transmission from the source and the relay by employing selection combining and maximal ratio combining, respectively. Specifically, we propose an optimal transmit antenna selection scheme to exploit the inherent spatial diversity gain for security enhancement. The closed-form expressions for the secrecy outage probability are derived to facilitate the system performance evaluation. At a more pragmatic level, we consider multiple users in the relay-aided MISO NOMA system and thus propose a user pairing algorithm to perfect successive interference cancellation. The algorithm avoids full search over all users by exploiting two-sided matching and low-complexity greed, thereby reducing the total complexity. Furthermore, aiming to maximize the secrecy rate, we formulate an optimization problem. Hence, the power allocation schemes are developed by jointly considering power limits and rate requirements. The scheme achieves closed-form solutions of power allocation for the data rate requirements of each user. Finally, simulation results validate the accuracy of the derived analysis and the improvement significant in secrecy performance by the proposed algorithm and scheme.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1617-1628"},"PeriodicalIF":4.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1109/JSYST.2024.3412985
Xuguang Hu;Junkai Zhang;Dazhong Ma;Qingchen Wang;Qiuye Sun
With the active participation of numerous end-users in the development of low-carbon energy ecosystems, the continuous expansion of the Energy Internet diminishes the timeliness of energy transmission and increases the complexity of energy scheduling, which leads to reduced energy efficiency. To solve it, a partitioning approach based on dual-stage agglomeration for Energy Internet is proposed in this article. First, the entropy weight of Energy Internet is proposed to assess the line significance of energy transmission, while establishing a uniform criterion of judgment by considering the energy loss of heterogeneous energy sources. Second, as the first stage of partitioning, the local expansion and boundary detection mechanism is proposed to realize localized node agglomeration and generate small-scale regions while ensuring all nodes contained in subregions. Furthermore, the hierarchical region agglomeration mechanism is proposed as the second stage of partitioning, which can aggregate the generated small-scale regions and improve the quality of the partitioning result based on flexible partitioning. Through the above stages, the proposed partitioning approach improves energy allocation, transmission and global efficiency of Energy Internet. Finally, case studies of an Energy Internet with 171-node are presented to validate the proposed approach.
{"title":"Dual-Stage Agglomeration Strategy: An Approach of Flexible Partitioning for Energy Internet","authors":"Xuguang Hu;Junkai Zhang;Dazhong Ma;Qingchen Wang;Qiuye Sun","doi":"10.1109/JSYST.2024.3412985","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3412985","url":null,"abstract":"With the active participation of numerous end-users in the development of low-carbon energy ecosystems, the continuous expansion of the Energy Internet diminishes the timeliness of energy transmission and increases the complexity of energy scheduling, which leads to reduced energy efficiency. To solve it, a partitioning approach based on dual-stage agglomeration for Energy Internet is proposed in this article. First, the entropy weight of Energy Internet is proposed to assess the line significance of energy transmission, while establishing a uniform criterion of judgment by considering the energy loss of heterogeneous energy sources. Second, as the first stage of partitioning, the local expansion and boundary detection mechanism is proposed to realize localized node agglomeration and generate small-scale regions while ensuring all nodes contained in subregions. Furthermore, the hierarchical region agglomeration mechanism is proposed as the second stage of partitioning, which can aggregate the generated small-scale regions and improve the quality of the partitioning result based on flexible partitioning. Through the above stages, the proposed partitioning approach improves energy allocation, transmission and global efficiency of Energy Internet. Finally, case studies of an Energy Internet with 171-node are presented to validate the proposed approach.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1560-1569"},"PeriodicalIF":4.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the advancement of edge computing, more and more intelligent applications are being deployed at the edge in proximity to end devices to provide in-vehicle services. However, the implementation of some complex services requires the collaboration of multiple AI models to handle and analyze various types of sensory data. In this context, the simultaneous scheduling and execution of multiple model inference tasks is an emerging scenario and faces many challenges. One of the major challenges is to reduce the completion time of time-sensitive services. In order to solve this problem, a multiagent reinforcement learning-based multimodel inference task scheduling method was proposed in this article, with a newly designed reward function to jointly optimize the overall running time and load imbalance. First, the multiagent proximal policy optimization algorithm is utilized for designing the task scheduling method. Second, the designed method can generate near-optimal task scheduling decisions and then dynamically allocate inference tasks to different edge applications based on their status and task characteristics. Third, one assessment index, quality of method, is defined and the proposed method is compared with the other five benchmark methods. Experimental results reveal that the proposed method can reduce the running time of multimodel inference by at least 25% or more, closing to the optimal solution.
{"title":"Multiagent Reinforcement Learning-Based Multimodel Running Latency Optimization in Vehicular Edge Computing Paradigm","authors":"Peisong Li;Ziren Xiao;Xinheng Wang;Muddesar Iqbal;Pablo Casaseca-de-la-Higuera","doi":"10.1109/JSYST.2024.3407213","DOIUrl":"10.1109/JSYST.2024.3407213","url":null,"abstract":"With the advancement of edge computing, more and more intelligent applications are being deployed at the edge in proximity to end devices to provide in-vehicle services. However, the implementation of some complex services requires the collaboration of multiple AI models to handle and analyze various types of sensory data. In this context, the simultaneous scheduling and execution of multiple model inference tasks is an emerging scenario and faces many challenges. One of the major challenges is to reduce the completion time of time-sensitive services. In order to solve this problem, a multiagent reinforcement learning-based multimodel inference task scheduling method was proposed in this article, with a newly designed reward function to jointly optimize the overall running time and load imbalance. First, the multiagent proximal policy optimization algorithm is utilized for designing the task scheduling method. Second, the designed method can generate near-optimal task scheduling decisions and then dynamically allocate inference tasks to different edge applications based on their status and task characteristics. Third, one assessment index, quality of method, is defined and the proposed method is compared with the other five benchmark methods. Experimental results reveal that the proposed method can reduce the running time of multimodel inference by at least 25% or more, closing to the optimal solution.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1860-1870"},"PeriodicalIF":4.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/JSYST.2024.3408607
Lihong Feng;Bonan Huang;Xiangpeng Xie
This article investigates the output containment problem for nonlinear heterogeneous multiagent systems subjected to actuator faults. The dynamics of followers are modeled by Takagi–Sugeno (T–S) fuzzy systems, these models are effective in handling a wide range of nonlinearities. First, to address the challenge of limited information interaction between followers and leaders, a distributed compensator is developed to estimate the convex hull information derived from the leaders' states. Furthermore, a dynamic event-triggered mechanism combined with a sampler is employed to eliminate unnecessary continuous transmission, thereby reducing the communication burden and saving energy. Subsequently, fuzzy controllers are devised for the followers based on the output information and the states of compensators, ensuring the output containment of the T–S fuzzy system and preventing the propagation of actuator faults. The Lyapunov stability theory is utilized to derive rigorous convergence conditions for the system, and then, gain matrices are obtained in terms of linear matrix inequalities. A numerical simulation and a tunnel diode network circuit model simulation are provided to demonstrate the effectiveness and superiority of the proposed controller.
{"title":"Dynamic Event-Triggered Containment Control for T–S Fuzzy Multiagent Systems With Actuator Faults","authors":"Lihong Feng;Bonan Huang;Xiangpeng Xie","doi":"10.1109/JSYST.2024.3408607","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3408607","url":null,"abstract":"This article investigates the output containment problem for nonlinear heterogeneous multiagent systems subjected to actuator faults. The dynamics of followers are modeled by Takagi–Sugeno (T–S) fuzzy systems, these models are effective in handling a wide range of nonlinearities. First, to address the challenge of limited information interaction between followers and leaders, a distributed compensator is developed to estimate the convex hull information derived from the leaders' states. Furthermore, a dynamic event-triggered mechanism combined with a sampler is employed to eliminate unnecessary continuous transmission, thereby reducing the communication burden and saving energy. Subsequently, fuzzy controllers are devised for the followers based on the output information and the states of compensators, ensuring the output containment of the T–S fuzzy system and preventing the propagation of actuator faults. The Lyapunov stability theory is utilized to derive rigorous convergence conditions for the system, and then, gain matrices are obtained in terms of linear matrix inequalities. A numerical simulation and a tunnel diode network circuit model simulation are provided to demonstrate the effectiveness and superiority of the proposed controller.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1538-1548"},"PeriodicalIF":4.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
More developed marine sensors for various applications has induced a rapid increase in marine data. The feedback from these marine data becomes challenging due to the backward marine communication techniques. The space–air–ground–sea integrated network (SAGSIN) provides a possible solution to solve this challenge by making use of the advantages of different networks. However, how to coordinate these networks and manage heterogeneous resources to satisfy the communication requirements of different marine applications remains to be solved. In this article, we investigate the resource management problem of SAGSIN for marine applications. A resource management architecture is proposed in which software-defined networking (SDN) controllers are employed. Based on this architecture, heterogeneous resources can be scheduled, and the data from devices with different communication modes can be transmitted via SAGSIN without changing the communication mode of the devices. We further propose two multiagent deep reinforcement learning resource management schemes to help individual devices find optimal access and resource allocation decisions to feed their data back to the terrestrial data centers. The design of these proposed schemes fully considers the scarce communication resources of marine scenarios, which makes data feedback more communication efficient while satisfying quality of service (QoS) requirements. Simulation results show that the improved MA_SDN_Centralized resource management scheme can significantly reduce the blocking probability of the system with guaranteed QoS, while reducing the communication overhead of learning.
{"title":"Resource Management for QoS-Guaranteed Marine Data Feedback Based on Space–Air–Ground–Sea Network","authors":"Yuanmo Lin;Zhiyong Xu;Jianhua Li;Jingyuan Wang;Cheng Li;Zhonghu Huang;Yanli Xu","doi":"10.1109/JSYST.2024.3439343","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3439343","url":null,"abstract":"More developed marine sensors for various applications has induced a rapid increase in marine data. The feedback from these marine data becomes challenging due to the backward marine communication techniques. The space–air–ground–sea integrated network (SAGSIN) provides a possible solution to solve this challenge by making use of the advantages of different networks. However, how to coordinate these networks and manage heterogeneous resources to satisfy the communication requirements of different marine applications remains to be solved. In this article, we investigate the resource management problem of SAGSIN for marine applications. A resource management architecture is proposed in which software-defined networking (SDN) controllers are employed. Based on this architecture, heterogeneous resources can be scheduled, and the data from devices with different communication modes can be transmitted via SAGSIN without changing the communication mode of the devices. We further propose two multiagent deep reinforcement learning resource management schemes to help individual devices find optimal access and resource allocation decisions to feed their data back to the terrestrial data centers. The design of these proposed schemes fully considers the scarce communication resources of marine scenarios, which makes data feedback more communication efficient while satisfying quality of service (QoS) requirements. Simulation results show that the improved MA_SDN_Centralized resource management scheme can significantly reduce the blocking probability of the system with guaranteed QoS, while reducing the communication overhead of learning.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1741-1752"},"PeriodicalIF":4.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing number of electric vehicles (EVs) on the roads led to a wide deployment of public EV charging stations (EVCSs). Recent reports revealed that both EVs and EVCSs are targets of cyber-attacks. In this context, a malware attack on vehicle-to-grid (V2G) communications increases the risk of malware spread among EVs and public EVCSs. However, the existing literature lacks practical studies on malware spread in power-transportation systems. Hence, this article demonstrates malicious traffic injection and proposes strategies to identify target EVCSs that can maximize physical malware spread within power-transportation systems. We first show the feasibility of injecting malicious traffic into the front-end V2G communication. Next, we establish a model that reflects the logical connectivity among the EVCSs, based on a realistic framework for large-scale EV commute and charge simulation. The logical connectivity is then translated into a malware spread probability, which we use to design an optimal attack strategy that identifies the locations of target EVCSs that maximize the malware spread. We compare malware spread due to random, cluster-based, and optimal attack strategies in both urban (Nashville) and rural (Cookeville) U.S. cities. Our results reveal that optimal attack strategies can accelerate malware spread by $10%$ – $33%$ .
{"title":"Attack Design for Maximum Malware Spread Through EVs Commute and Charge in Power-Transportation Systems","authors":"Sushil Poudel;Mahmoud Abouyoussef;J. Eileen Baugh;Muhammad Ismail","doi":"10.1109/JSYST.2024.3446231","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3446231","url":null,"abstract":"The growing number of electric vehicles (EVs) on the roads led to a wide deployment of public EV charging stations (EVCSs). Recent reports revealed that both EVs and EVCSs are targets of cyber-attacks. In this context, a malware attack on vehicle-to-grid (V2G) communications increases the risk of malware spread among EVs and public EVCSs. However, the existing literature lacks practical studies on malware spread in power-transportation systems. Hence, this article demonstrates malicious traffic injection and proposes strategies to identify target EVCSs that can maximize physical malware spread within power-transportation systems. We first show the feasibility of injecting malicious traffic into the front-end V2G communication. Next, we establish a model that reflects the logical connectivity among the EVCSs, based on a realistic framework for large-scale EV commute and charge simulation. The logical connectivity is then translated into a malware spread probability, which we use to design an optimal attack strategy that identifies the locations of target EVCSs that maximize the malware spread. We compare malware spread due to random, cluster-based, and optimal attack strategies in both urban (Nashville) and rural (Cookeville) U.S. cities. Our results reveal that optimal attack strategies can accelerate malware spread by \u0000<inline-formula><tex-math>$10%$</tex-math></inline-formula>\u0000–\u0000<inline-formula><tex-math>$33%$</tex-math></inline-formula>\u0000.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1809-1820"},"PeriodicalIF":4.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1109/JSYST.2024.3443541
Ziyi Zhou;Oluwakayode Onireti;Xinyi Lin;Lei Zhang;Muhammad Ali Imran
The ever-increasing mobile users pose a great challenge to the current centralized structure, where the failure of the network center significantly compromises privacy and data security. Practical byzantine fault tolerant (PBFT), a voting-based consensus blockchain, is a plausible solution to the wireless network for its distributed and decentralized traits, and it is not computation-intensive. The view change mechanism guarantees the liveness and resilience of PBFT, but it also causes delay. Moreover, spectrum is usually shared in the wireless network, introducing additional channel contention. Hence, we develop a framework investigating the impacts of channel contention on the wireless PBFT network using the IEEE 802.11 protocol. Based on the Markov model, we derive the throughput, transmission success probability, and transaction confirmation delay of such a network. Furthermore, we derive the view change delay in reference to the transaction confirmation delay. The analysis and results show that channel contention impacts in two ways. It impairs the success probability and increases the chance of view change. Optimal pairs of packet arrival rate and contention window size are formulated to maximize the consensus's success probability without sacrificing the overall network performance. Further, the optimal pairs under different network sizes are demonstrated for straightforward guidance.
{"title":"On the Performance of Wireless PBFT-Based Blockchain Network With IEEE 802.11","authors":"Ziyi Zhou;Oluwakayode Onireti;Xinyi Lin;Lei Zhang;Muhammad Ali Imran","doi":"10.1109/JSYST.2024.3443541","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3443541","url":null,"abstract":"The ever-increasing mobile users pose a great challenge to the current centralized structure, where the failure of the network center significantly compromises privacy and data security. Practical byzantine fault tolerant (PBFT), a voting-based consensus blockchain, is a plausible solution to the wireless network for its distributed and decentralized traits, and it is not computation-intensive. The view change mechanism guarantees the liveness and resilience of PBFT, but it also causes delay. Moreover, spectrum is usually shared in the wireless network, introducing additional channel contention. Hence, we develop a framework investigating the impacts of channel contention on the wireless PBFT network using the IEEE 802.11 protocol. Based on the Markov model, we derive the throughput, transmission success probability, and transaction confirmation delay of such a network. Furthermore, we derive the view change delay in reference to the transaction confirmation delay. The analysis and results show that channel contention impacts in two ways. It impairs the success probability and increases the chance of view change. Optimal pairs of packet arrival rate and contention window size are formulated to maximize the consensus's success probability without sacrificing the overall network performance. Further, the optimal pairs under different network sizes are demonstrated for straightforward guidance.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1786-1797"},"PeriodicalIF":4.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1109/JSYST.2024.3444049
Xin Cai;Bingpeng Gao;Xinyuan Nan;Jie Yuan
For islanded ac microgrids with constrained digital communication networks under denial-of-service (DoS) attacks, this article proposes a resilient distributed secondary control strategy with a discrete-time quantization communication scheme to enhance the resilience to DoS attacks and to reduce communication loads. To cope with the time-constrained DoS attacks, the proposed discrete-time communication scheme combines a periodic communication attempt in the time intervals in presence of DoS attacks, and an event-triggered communication in the time intervals in absence of DoS attacks. Moreover, a dynamic quantization scheme for data transmission is introduced in the distributed control for the stability of the frequency and voltage at the reference. Then, a sufficient stability condition is established for the microgrid and the tradeoff between the communication attempt period and DoS attacks is obtained. Simulations of a microgrid are presented to verify the proposed resilient secondary control strategy.
对于在拒绝服务（DoS）攻击下具有受限数字通信网络的孤岛交流微电网，本文提出了一种具有离散时间量化通信方案的弹性分布式二次控制策略，以增强对 DoS 攻击的弹性并减少通信负载。为应对时间受限的 DoS 攻击，所提出的离散时间通信方案结合了在存在 DoS 攻击的时间间隔内的周期性通信尝试和在不存在 DoS 攻击的时间间隔内的事件触发通信。此外，在分布式控制中引入了数据传输动态量化方案，以确保频率和电压在基准点的稳定性。然后，建立了微电网的充分稳定条件，并得到了通信尝试周期与 DoS 攻击之间的权衡。本文还对微电网进行了仿真，以验证所提出的弹性二次控制策略。
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