Pub Date : 2025-01-16DOI: 10.1016/j.vehcom.2025.100877
Man Zhou, Jie Tian, Dongyang Li, Tiantian Li, Ji Bian
Vehicular edge computing (VEC) is beneficial to reduce task offloading delay and service acquisition delay by pushing cloud functions to the edge of the networks. Edge servers have the computation and storage capacity to execute vehicular tasks and cache the services required for tasks execution. Due to the limited caching resources of a single edge server, the vehicles will obtain services from cloud servers when they can't obtain from their own associated edge server, which results to the increase of service acquisition delay. To this end, we establish a multiple edge servers collaboration caching framework to minimize the heterogeneity tasks execution delay of the all vehicles, including tasks offloading delay, services acquisition delay and tasks processing delay. Specifically, the edge servers collaboratively make slot level caching decisions, i.e., what to be cached in each slot according to vehicular tasks requirements. Based on this framework, we formulate a long-term optimization problem to minimize the heterogeneity tasks execution delay of the all vehicles under the long-term energy constraints. To solve it, we firstly construct a virtual energy deficit queue, and then we transform the target problem into a delay drift-plus-energy consumption minimization problem by utilizing Lyapunov optimization theory. The equal transformation problem is a 0-1 multi-knapsack problem, which is a NP-hardness problem. To solve it, we improved the greedy algorithm that retains the selection process of the greedy algorithm and the comparison and selection of the genetic algorithm. Extensive simulations illustrate that the proposed scheme achieves near optimal delay performance while strictly satisfying long-term energy constraints, and outperforms other baseline schemes in terms of time-averaged delay and time-averaged energy consumption.
{"title":"Collaborative service caching for delay minimization in vehicular edge computing networks","authors":"Man Zhou, Jie Tian, Dongyang Li, Tiantian Li, Ji Bian","doi":"10.1016/j.vehcom.2025.100877","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100877","url":null,"abstract":"Vehicular edge computing (VEC) is beneficial to reduce task offloading delay and service acquisition delay by pushing cloud functions to the edge of the networks. Edge servers have the computation and storage capacity to execute vehicular tasks and cache the services required for tasks execution. Due to the limited caching resources of a single edge server, the vehicles will obtain services from cloud servers when they can't obtain from their own associated edge server, which results to the increase of service acquisition delay. To this end, we establish a multiple edge servers collaboration caching framework to minimize the heterogeneity tasks execution delay of the all vehicles, including tasks offloading delay, services acquisition delay and tasks processing delay. Specifically, the edge servers collaboratively make slot level caching decisions, i.e., what to be cached in each slot according to vehicular tasks requirements. Based on this framework, we formulate a long-term optimization problem to minimize the heterogeneity tasks execution delay of the all vehicles under the long-term energy constraints. To solve it, we firstly construct a virtual energy deficit queue, and then we transform the target problem into a delay drift-plus-energy consumption minimization problem by utilizing Lyapunov optimization theory. The equal transformation problem is a 0-1 multi-knapsack problem, which is a NP-hardness problem. To solve it, we improved the greedy algorithm that retains the selection process of the greedy algorithm and the comparison and selection of the genetic algorithm. Extensive simulations illustrate that the proposed scheme achieves near optimal delay performance while strictly satisfying long-term energy constraints, and outperforms other baseline schemes in terms of time-averaged delay and time-averaged energy consumption.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"122 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-15DOI: 10.1016/j.vehcom.2025.100884
Siji Chen, Bo Jiang, Hong Xu, Tao Pang, Mingke Gao, Ziyang Liu
Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to be used in industries and various sectors of human life to provide a wide range of applications and services, significantly enhancing its applicability in different fields. When a UAV swarm performs complex tasks, flying Ad-hoc networks (FANETs) based on cluster structures have become a key research topic in the field of topology control due to their strong scalability and low routing overhead. However, current research mainly concentrates on the selection of the cluster head (CH), considering all UAVs within the CH's communication radius as cluster members (CMs), often neglecting whether the cluster can effectively accomplish the task, thereby potentially leading to mission failure. To overcome this problem, this paper innovatively proposes a task-driven clustering (TDC-MOPSO) algorithm based on improved multi-objective particle swarm optimization (MOPSO) for clustering-structure-based heterogeneous FANETs, which introduces the transfer function to improve the search range of particles and the mutation mechanism to avoid falling into local optima, and a more reasonable fitness function is designed to select CHs. The simulation results indicate that the proposed TDC-MOPSO algorithm dramatically improves the task completion rate by up to about 41.32% and extends the node lifetime by up to about 50.12% compared to traditional clustering algorithms. Meanwhile, the TDC-MOPSO algorithm improves the task completion rate by up to about 11.02% compared to other mopso-based algorithms. Furthermore, the TDC-MOPSO algorithm obtains more clustering solutions with higher average energy, less waste of resources, less CH handover rate, and less routing overhead in simulation. The proposed algorithm is also verified in a real-life scenario, which also effectively supports the completion of the task. All of which demonstrates that the TDC-MOPSO algorithm enhances the efficiency of task execution while ensuring communication performance for clustering-structure-based FANETs.
{"title":"A task-driven scheme for forming clustering-structure-based heterogeneous FANETs","authors":"Siji Chen, Bo Jiang, Hong Xu, Tao Pang, Mingke Gao, Ziyang Liu","doi":"10.1016/j.vehcom.2025.100884","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100884","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to be used in industries and various sectors of human life to provide a wide range of applications and services, significantly enhancing its applicability in different fields. When a UAV swarm performs complex tasks, flying Ad-hoc networks (FANETs) based on cluster structures have become a key research topic in the field of topology control due to their strong scalability and low routing overhead. However, current research mainly concentrates on the selection of the cluster head (CH), considering all UAVs within the CH's communication radius as cluster members (CMs), often neglecting whether the cluster can effectively accomplish the task, thereby potentially leading to mission failure. To overcome this problem, this paper innovatively proposes a task-driven clustering (TDC-MOPSO) algorithm based on improved multi-objective particle swarm optimization (MOPSO) for clustering-structure-based heterogeneous FANETs, which introduces the transfer function to improve the search range of particles and the mutation mechanism to avoid falling into local optima, and a more reasonable fitness function is designed to select CHs. The simulation results indicate that the proposed TDC-MOPSO algorithm dramatically improves the task completion rate by up to about 41.32% and extends the node lifetime by up to about 50.12% compared to traditional clustering algorithms. Meanwhile, the TDC-MOPSO algorithm improves the task completion rate by up to about 11.02% compared to other mopso-based algorithms. Furthermore, the TDC-MOPSO algorithm obtains more clustering solutions with higher average energy, less waste of resources, less CH handover rate, and less routing overhead in simulation. The proposed algorithm is also verified in a real-life scenario, which also effectively supports the completion of the task. All of which demonstrates that the TDC-MOPSO algorithm enhances the efficiency of task execution while ensuring communication performance for clustering-structure-based FANETs.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"37 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Autonomous vehicles for intelligent surveillance in rural areas increasingly demand low-cost and reliable data collection technologies to perform dense monitoring across extended areas. Backscattering communication has been employed for this purpose, primarily for low-cost and energy efficiency reasons. This paper considers a backscattering data collection system empowered by unmanned aerial vehicles (UAVs) to overcome the challenge of wireless coverage and provide backscattering tags with physical-layer security. Relevant prior works only focused on the secrecy of backscattering communications, while the limited battery of UAVs was overlooked during the underlying vehicle control. This paper aims to jointly optimize the trajectory of multiple UAVs and choice of tags, as well as tags' reflection parameters, to manage data leakage and total energy consumed by UAVs during a round of data collection. Our specific contributions are threefold. (1) We propose a 3D multi-UAV backscattering data collection framework and formulate an optimization problem to maximize the ratio of secrecy across all tags to the power consumption of UAVs subject to some practical constraints. (2) We show that our problem is non-convex and partition it into three sub-problems, transform objective functions, and relax certain constraints to obtain approximate convex problems that yield suboptimal solutions. (3) We evaluate the efficacy of our proposed intelligent security protocol for UAV-assisted data collection, compare its performance with some baseline schemes, our protocal achieve leading performance in terms of secrecy energy efficiency. We also provide the impact of parameters on the secrecy energy efficiency, as well as quantify its complexity via extensive simulations.
{"title":"Fairness aware secure energy efficiency maximization for UAV-assisted data collection in backscattering networks","authors":"Jiawang Zeng, Deepak Mishra, Hassan Habibi Gharakheili, Aruna Seneviratne","doi":"10.1016/j.vehcom.2025.100881","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100881","url":null,"abstract":"Autonomous vehicles for intelligent surveillance in rural areas increasingly demand low-cost and reliable data collection technologies to perform dense monitoring across extended areas. Backscattering communication has been employed for this purpose, primarily for low-cost and energy efficiency reasons. This paper considers a backscattering data collection system empowered by unmanned aerial vehicles (UAVs) to overcome the challenge of wireless coverage and provide backscattering tags with physical-layer security. Relevant prior works only focused on the secrecy of backscattering communications, while the limited battery of UAVs was overlooked during the underlying vehicle control. This paper aims to jointly optimize the trajectory of multiple UAVs and choice of tags, as well as tags' reflection parameters, to manage data leakage and total energy consumed by UAVs during a round of data collection. Our specific contributions are threefold. (1) We propose a 3D multi-UAV backscattering data collection framework and formulate an optimization problem to maximize the ratio of secrecy across all tags to the power consumption of UAVs subject to some practical constraints. (2) We show that our problem is non-convex and partition it into three sub-problems, transform objective functions, and relax certain constraints to obtain approximate convex problems that yield suboptimal solutions. (3) We evaluate the efficacy of our proposed intelligent security protocol for UAV-assisted data collection, compare its performance with some baseline schemes, our protocal achieve leading performance in terms of secrecy energy efficiency. We also provide the impact of parameters on the secrecy energy efficiency, as well as quantify its complexity via extensive simulations.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"205 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the shortage of energy resources and computational capability, unmanned aerial vehicles (UAVs) tend to fail to execute tasks with time-delay sensitive and complex demands like artificial intelligence (AI) enabled applications. Most offloading method literature in ground-air cooperative systems simply uses edge servers or remote cloud servers to provide computation resources and storage space. Unfortunately, their performance degrades since it is difficult to guarantee UAV's quality of experience (QoE) considering the long-distance transmission delay. To address this issue, this paper proposes a ground-air cooperative edge computing framework in which multiprocessing computation is implemented by the UAVs locally or offloads specific calculations to the edge server on unmanned ground vehicles (UGVs). The proposed framework consists of two innovative mechanisms: one is to consider a mobility-aware link prediction method and other indicators, including compute capacity and workload, to ensure a stable offloading environment, the another is to propose an energy-efficient distributed computation offloading algorithm (EDCOA) by modelling the computation offloading issue for UAVs as an analytical optimization problem. By offloading subtasks to multiple UGV nodes for multiprocessing, UAVs can leverage the computation resources of the surrounding edge network entities to enhance their computational capabilities. Extensive experiments and comparisons with state-of-the-art realtime offloading methods showed that the proposed framework outperforms other approaches by delivering better performance in reducing UAV energy consumption, ensuring successful task offloading rates and meeting latency requirements.
{"title":"An energy-efficient distributed computation offloading algorithm for ground-air cooperative networks","authors":"Yanling Shao, Hairui Xu, Liming Liu, Wenyong Dong, Pingping Shan, Junying Guo, Wenxuan Xu","doi":"10.1016/j.vehcom.2025.100875","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100875","url":null,"abstract":"Due to the shortage of energy resources and computational capability, unmanned aerial vehicles (UAVs) tend to fail to execute tasks with time-delay sensitive and complex demands like artificial intelligence (AI) enabled applications. Most offloading method literature in ground-air cooperative systems simply uses edge servers or remote cloud servers to provide computation resources and storage space. Unfortunately, their performance degrades since it is difficult to guarantee UAV's quality of experience (QoE) considering the long-distance transmission delay. To address this issue, this paper proposes a ground-air cooperative edge computing framework in which multiprocessing computation is implemented by the UAVs locally or offloads specific calculations to the edge server on unmanned ground vehicles (UGVs). The proposed framework consists of two innovative mechanisms: one is to consider a mobility-aware link prediction method and other indicators, including compute capacity and workload, to ensure a stable offloading environment, the another is to propose an energy-efficient distributed computation offloading algorithm (EDCOA) by modelling the computation offloading issue for UAVs as an analytical optimization problem. By offloading subtasks to multiple UGV nodes for multiprocessing, UAVs can leverage the computation resources of the surrounding edge network entities to enhance their computational capabilities. Extensive experiments and comparisons with state-of-the-art realtime offloading methods showed that the proposed framework outperforms other approaches by delivering better performance in reducing UAV energy consumption, ensuring successful task offloading rates and meeting latency requirements.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"12 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-01-13DOI: 10.1016/j.vehcom.2025.100878
Bruno Mendes, Marco Araújo, Adriano Goes, Daniel Corujo, Arnaldo S.R. Oliveira
Vehicle-to-Everything (V2X) communications are constrained by both 3GPP technical specifications, as well as by country-specific spectrum regulations. The world's largest economies, such as the USA, EU and China have self-imposed regulations regarding the specific bandwidths and central spectrum frequencies where both safety and non-safety related V2X communication services are allowed to occur (always aligned with the aforementioned 3GPP technical specifications). Although the channels used for safety, non-safety, and control packets differ, what all of these countries have in common is that V2X shall occur mostly on New Radio Unlicensed (NR-U) spectrum, i.e., by means of private networks. A specific bandwidth in the public spectrum is also available, but since public spectrum is purchased through auctions, it is quite common the case that one particular operator will own the entirety of this spectrum, leading to a monopoly in V2X operations. Besides, this public spectrum is quite limited in bandwidth. This of course includes all of the Intelligent Transportation Systems (ITS) services, even location-based services, such as the ones that require the usage of positioning technologies, like autonomous vehicles, that require said services in order to support complex maneuvers and cooperative driving. Global Navigation Satellite Systems (GNSS) such as GPS or Galileo, currently already offer high-accuracy location to vehicles. However, this form of stand-alone position estimation of the vehicle has several drawbacks, as the information is constrained to the individual vehicle and not shared with others in a secure manner. This exchange of position information between other entities (not only vehicles, but also other infrastructure nodes) is vital for actions such as cooperative maneuvers and to counter loss of satellite sight (e.g., when entering a tunnel). Taking these facts into consideration, it is therefore expected that in the mid to long-term, municipalities and highways will possess dedicated private 5G networks for V2X operations with the aim of offering a plethora of vehicular services, including positioning ones. Since the existent scientific literature lacks an integrated analysis of precise positioning services for ITS in 5G private networks, we propose in this paper, to provide a comprehensive review connecting these diverse elements, examining the role of 5G private networks in transmitting positioning messages in V2X scenarios. Additionally, the paper shall explore hybrid positioning systems that combine 5G and GNSS technologies, illustrating their potential to enhance V2X communications. This study offers a roadmap for the evolution of ITS and V2X communications by showcasing current trends and identifying areas for further research.
{"title":"Exploring V2X in 5G networks: A comprehensive survey of location-based services in hybrid scenarios","authors":"Bruno Mendes, Marco Araújo, Adriano Goes, Daniel Corujo, Arnaldo S.R. Oliveira","doi":"10.1016/j.vehcom.2025.100878","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100878","url":null,"abstract":"Vehicle-to-Everything (V2X) communications are constrained by both 3GPP technical specifications, as well as by country-specific spectrum regulations. The world's largest economies, such as the USA, EU and China have self-imposed regulations regarding the specific bandwidths and central spectrum frequencies where both safety and non-safety related V2X communication services are allowed to occur (always aligned with the aforementioned 3GPP technical specifications). Although the channels used for safety, non-safety, and control packets differ, what all of these countries have in common is that V2X shall occur mostly on New Radio Unlicensed (NR-U) spectrum, i.e., by means of private networks. A specific bandwidth in the public spectrum is also available, but since public spectrum is purchased through auctions, it is quite common the case that one particular operator will own the entirety of this spectrum, leading to a monopoly in V2X operations. Besides, this public spectrum is quite limited in bandwidth. This of course includes all of the Intelligent Transportation Systems (ITS) services, even location-based services, such as the ones that require the usage of positioning technologies, like autonomous vehicles, that require said services in order to support complex maneuvers and cooperative driving. Global Navigation Satellite Systems (GNSS) such as GPS or Galileo, currently already offer high-accuracy location to vehicles. However, this form of stand-alone position estimation of the vehicle has several drawbacks, as the information is constrained to the individual vehicle and not shared with others in a secure manner. This exchange of position information between other entities (not only vehicles, but also other infrastructure nodes) is vital for actions such as cooperative maneuvers and to counter loss of satellite sight (e.g., when entering a tunnel). Taking these facts into consideration, it is therefore expected that in the mid to long-term, municipalities and highways will possess dedicated private 5G networks for V2X operations with the aim of offering a plethora of vehicular services, including positioning ones. Since the existent scientific literature lacks an integrated analysis of precise positioning services for ITS in 5G private networks, we propose in this paper, to provide a comprehensive review connecting these diverse elements, examining the role of 5G private networks in transmitting positioning messages in V2X scenarios. Additionally, the paper shall explore hybrid positioning systems that combine 5G and GNSS technologies, illustrating their potential to enhance V2X communications. This study offers a roadmap for the evolution of ITS and V2X communications by showcasing current trends and identifying areas for further research.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"22 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-10DOI: 10.1016/j.vehcom.2024.100863
Zahraa Tarek, Mona Gafar, Shahenda Sarhan, Abdullah M. Shaheen, Ahmed S. Alwakeel
Reconfigurable Intelligent Surfaces (RISs) provide a promising avenue for enhancing performance and implementation efficiency in multiuser wireless communication systems by enabling the manipulation of radio wave propagation. In this paper, an Augmented Jellyfish Search Optimization Algorithm (AJFSOA) is specifically designed to optimize the achievable rate in RIS-equipped systems. AJFSOA distinguishes itself from previous approaches through the integration of a novel quasi-reflection operator, which aids in escaping local optima, and an adaptive neighborhood search mechanism that improves the algorithm's exploitation capabilities. These enhancements enable AJFSOA to efficiently refine promising solutions near the current best solution. Unlike prior research, our work explores two objective models: maximizing the average achievable rate for all users to ensure balanced system performance and maximizing the minimum achievable rate for individual users to improve worst-case scenarios. The comprehensive analysis demonstrates that AJFSOA effectively increases system capacity and supports a larger number of users simultaneously. An extensive testing is performed on communication systems with twenty and fifty users, comparing AJFSOA's performance against existing algorithms, including the standard JFSOA, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Differential Evolution (DE). Results show that AJFSOA outperforms the other algorithms significantly, with improvements of 26.59%, 9.75%, 14.71%, 0.29% and 13.52% over JFSOA, PSO, ACO, GA and DE, respectively, for the first objective model, and 21.66%, 10.6%, .17.44%, 2.71% and 22.36% for the second model. These findings highlight the distinct advantages and superior performance of the presented AJFSOA in efficient optimizing multiuser wireless networks.
{"title":"RIS-aided jellyfish search optimization for multiuser wireless networks improvement","authors":"Zahraa Tarek, Mona Gafar, Shahenda Sarhan, Abdullah M. Shaheen, Ahmed S. Alwakeel","doi":"10.1016/j.vehcom.2024.100863","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100863","url":null,"abstract":"Reconfigurable Intelligent Surfaces (RISs) provide a promising avenue for enhancing performance and implementation efficiency in multiuser wireless communication systems by enabling the manipulation of radio wave propagation. In this paper, an Augmented Jellyfish Search Optimization Algorithm (AJFSOA) is specifically designed to optimize the achievable rate in RIS-equipped systems. AJFSOA distinguishes itself from previous approaches through the integration of a novel quasi-reflection operator, which aids in escaping local optima, and an adaptive neighborhood search mechanism that improves the algorithm's exploitation capabilities. These enhancements enable AJFSOA to efficiently refine promising solutions near the current best solution. Unlike prior research, our work explores two objective models: maximizing the average achievable rate for all users to ensure balanced system performance and maximizing the minimum achievable rate for individual users to improve worst-case scenarios. The comprehensive analysis demonstrates that AJFSOA effectively increases system capacity and supports a larger number of users simultaneously. An extensive testing is performed on communication systems with twenty and fifty users, comparing AJFSOA's performance against existing algorithms, including the standard JFSOA, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Differential Evolution (DE). Results show that AJFSOA outperforms the other algorithms significantly, with improvements of 26.59%, 9.75%, 14.71%, 0.29% and 13.52% over JFSOA, PSO, ACO, GA and DE, respectively, for the first objective model, and 21.66%, 10.6%, .17.44%, 2.71% and 22.36% for the second model. These findings highlight the distinct advantages and superior performance of the presented AJFSOA in efficient optimizing multiuser wireless networks.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"42 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-07DOI: 10.1016/j.vehcom.2024.100871
Brooke Kidmose
The humble, mechanical automobile has gradually evolved into our modern connected and autonomous vehicles (CAVs)—also known as “smart vehicles.” Similarly, our cities are gradually developing into “smart cities,” where municipal services from transportation networks to utilities to recycling to law enforcement are integrated. The idea, with both smart vehicles and smart cities, is that more data leads to better, more informed decisions. Smart vehicles and smart cities would acquire data from their own equipment (e.g., cameras, sensors) and from their connections—e.g., connections to fellow smart vehicles, to road-side infrastructure, to smart transportation systems (STSs), etc.
{"title":"A review of smart vehicles in smart cities: Dangers, impacts, and the threat landscape","authors":"Brooke Kidmose","doi":"10.1016/j.vehcom.2024.100871","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100871","url":null,"abstract":"The humble, mechanical automobile has gradually evolved into our modern connected and autonomous vehicles (CAVs)—also known as “smart vehicles.” Similarly, our cities are gradually developing into “smart cities,” where municipal services from transportation networks to utilities to recycling to law enforcement are integrated. The idea, with both smart vehicles and smart cities, is that more data leads to better, more informed decisions. Smart vehicles and smart cities would acquire data from their own equipment (e.g., cameras, sensors) and from their connections—e.g., connections to fellow smart vehicles, to road-side infrastructure, to smart transportation systems (STSs), etc.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"11 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-06DOI: 10.1016/j.vehcom.2024.100870
Sawsan AbdulRahman, Safa Otoum, Ouns Bouachir
With the proliferation of Internet of Things, leveraging federated learning (FL) for collaborative model training has become paramount. It has turned into a powerful tool to analyze on-device data and produce real-time applications while safeguarding user privacy. However, in vehicular networks, the dynamic nature of vehicles, coupled with resource constraints, gives rise to new challenges for efficient FL implementation. In this paper, we address the critical problems of optimizing computational and communication resources and selecting the appropriate vehicle to participate in the process. Our proposed scheme bypasses the communication bottleneck by forming homogeneous groups based on the vehicles mobility/direction and their computing resources. Vehicle-to-Vehicle communication is then adapted within each group, and communication with an on-road edge node is orchestrated by a designated Cluster Head (CH). The latter is selected based on several factors, including connectivity index, mobility coherence, and computational resources. This selection process is designed to be robust against potential cheating attempts, which prevents nodes from avoiding the role of CH to conserve their resources. Moreover, we propose a matching algorithm that pairs each vehicular group with the appropriate edge nodes responsible for aggregating local models and facilitating communication with the server, which subsequently processes the models from all edges. The conducted experiments show promising results compared to benchmarks by achieving: (1) significantly higher amounts of trained data per iteration through strategic CH selection, leading to improved model accuracy and reduced communication overhead. Additionally, our approach demonstrates (2) efficient network load management, (3) faster convergence times in later training rounds, and (4) superior cluster stability.
{"title":"Federated learning on the go: Building stable clusters and optimizing resources on the road","authors":"Sawsan AbdulRahman, Safa Otoum, Ouns Bouachir","doi":"10.1016/j.vehcom.2024.100870","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100870","url":null,"abstract":"With the proliferation of Internet of Things, leveraging federated learning (FL) for collaborative model training has become paramount. It has turned into a powerful tool to analyze on-device data and produce real-time applications while safeguarding user privacy. However, in vehicular networks, the dynamic nature of vehicles, coupled with resource constraints, gives rise to new challenges for efficient FL implementation. In this paper, we address the critical problems of optimizing computational and communication resources and selecting the appropriate vehicle to participate in the process. Our proposed scheme bypasses the communication bottleneck by forming homogeneous groups based on the vehicles mobility/direction and their computing resources. Vehicle-to-Vehicle communication is then adapted within each group, and communication with an on-road edge node is orchestrated by a designated Cluster Head (CH). The latter is selected based on several factors, including connectivity index, mobility coherence, and computational resources. This selection process is designed to be robust against potential cheating attempts, which prevents nodes from avoiding the role of CH to conserve their resources. Moreover, we propose a matching algorithm that pairs each vehicular group with the appropriate edge nodes responsible for aggregating local models and facilitating communication with the server, which subsequently processes the models from all edges. The conducted experiments show promising results compared to benchmarks by achieving: (1) significantly higher amounts of trained data per iteration through strategic CH selection, leading to improved model accuracy and reduced communication overhead. Additionally, our approach demonstrates (2) efficient network load management, (3) faster convergence times in later training rounds, and (4) superior cluster stability.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"10 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-06DOI: 10.1016/j.vehcom.2024.100868
Chenmin Zhang, Yonggui Liu, Zeming Li
The vehicle platoon using the cooperative adaptive cruise control (CACC) transmits information between vehicles via communication networks to increase the control performance. However, time delays are inevitable during the network transmission of information, which influence the stability of the CACC vehicle system. This paper proposes a method for compensating information affected by time delays based on a Bi-LSTM model. First, the third-order dynamics of the CACC vehicle systems are established, and the control strategies are proposed with the leading, preceding and following vehicles. The conditions of local stability and string stability for the CACC vehicle systems without time delays are derived based on the Routh-Hurwitz stability criterion and the frequency domain methods, which reveal the relationship between the model parameters and the controller parameters. For the CACC vehicle systems with time delays, the maximum time delays that ensure the local stability and string stability are achieved using the similar methods accordingly. However, the stability of the CACC vehicle systems is destroyed, when the time delay exceeds the maximum value. To deal with the impact of time delays, the bidirectional long short term memory (Bi-LSTM) model is adopted to predict and reconstitute the information affected by time delays. Furthermore, the relevant parameters are set and the real vehicle data is used for calculation and simulation. The simulation results confirm the local and string stability can be ensured, and further show the boundary of the maximum time delay may reach 0.45s for the CACC vehicle systems in this paper. In order to highlight superiority of Bi-LSTM, by comparing LSTM and KF with BiLSTM, the simulation results show Bi-LSTM has the highest correlation coefficient and the smallest root mean square error, which verify that Bi-LSTM reconstructing information affected by time delays is more effective than KF and LSTM.
{"title":"The stability for CACC system with time delays and reconstitution information of vehicles for compensating delays based on Bi-LSTM","authors":"Chenmin Zhang, Yonggui Liu, Zeming Li","doi":"10.1016/j.vehcom.2024.100868","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100868","url":null,"abstract":"The vehicle platoon using the cooperative adaptive cruise control (CACC) transmits information between vehicles via communication networks to increase the control performance. However, time delays are inevitable during the network transmission of information, which influence the stability of the CACC vehicle system. This paper proposes a method for compensating information affected by time delays based on a Bi-LSTM model. First, the third-order dynamics of the CACC vehicle systems are established, and the control strategies are proposed with the leading, preceding and following vehicles. The conditions of local stability and string stability for the CACC vehicle systems without time delays are derived based on the Routh-Hurwitz stability criterion and the frequency domain methods, which reveal the relationship between the model parameters and the controller parameters. For the CACC vehicle systems with time delays, the maximum time delays that ensure the local stability and string stability are achieved using the similar methods accordingly. However, the stability of the CACC vehicle systems is destroyed, when the time delay exceeds the maximum value. To deal with the impact of time delays, the bidirectional long short term memory (Bi-LSTM) model is adopted to predict and reconstitute the information affected by time delays. Furthermore, the relevant parameters are set and the real vehicle data is used for calculation and simulation. The simulation results confirm the local and string stability can be ensured, and further show the boundary of the maximum time delay may reach 0.45<ce:italic>s</ce:italic> for the CACC vehicle systems in this paper. In order to highlight superiority of Bi-LSTM, by comparing LSTM and KF with BiLSTM, the simulation results show Bi-LSTM has the highest correlation coefficient and the smallest root mean square error, which verify that Bi-LSTM reconstructing information affected by time delays is more effective than KF and LSTM.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"22 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2024-12-06DOI: 10.1016/j.vehcom.2024.100869
Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen
This paper presents VeTraSPM (Vehicle Trajectory Data Sequential Pattern Mining), a novel algorithm designed to address the limitations of existing sequential pattern mining methods when applied to vehicle trajectory data. Current algorithms fail to capture essential characteristics such as directional flow on one-way roads (e.g., “AB” is valid but not “BA”), connectivity constraints at junctions, and the repetition of links within sequences. VeTraSPM overcomes these gaps by accurately extracting frequent patterns and confident rules while leveraging vertical projection for efficient memory and space management, enabling it to handle large datasets. Furthermore, the algorithm incorporates partitioning and parallelization techniques, further enhancing its scalability for real-world traffic environments. Three new metrics—FqMS, CMS, and SIS—are introduced to assess link criticality based on the consistent occurrence of links across movement patterns at various levels. The efficiency of VeTraSPM is demonstrated through a comparative analysis with baseline algorithms, showcasing its superior performance. The visualization of the proposed metrics offers valuable insights into link importance, supporting proactive traffic management strategies. A case study using real-world datasets from Luxembourg and Monaco validates its scalability and practical value in enhancing the resilience of urban traffic networks.
{"title":"VeTraSPM: Novel vehicle trajectory data sequential pattern mining algorithm for link criticality analysis","authors":"Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen","doi":"10.1016/j.vehcom.2024.100869","DOIUrl":"https://doi.org/10.1016/j.vehcom.2024.100869","url":null,"abstract":"This paper presents VeTraSPM (Vehicle Trajectory Data Sequential Pattern Mining), a novel algorithm designed to address the limitations of existing sequential pattern mining methods when applied to vehicle trajectory data. Current algorithms fail to capture essential characteristics such as directional flow on one-way roads (e.g., “AB” is valid but not “BA”), connectivity constraints at junctions, and the repetition of links within sequences. VeTraSPM overcomes these gaps by accurately extracting frequent patterns and confident rules while leveraging vertical projection for efficient memory and space management, enabling it to handle large datasets. Furthermore, the algorithm incorporates partitioning and parallelization techniques, further enhancing its scalability for real-world traffic environments. Three new metrics—FqMS, CMS, and SIS—are introduced to assess link criticality based on the consistent occurrence of links across movement patterns at various levels. The efficiency of VeTraSPM is demonstrated through a comparative analysis with baseline algorithms, showcasing its superior performance. The visualization of the proposed metrics offers valuable insights into link importance, supporting proactive traffic management strategies. A case study using real-world datasets from Luxembourg and Monaco validates its scalability and practical value in enhancing the resilience of urban traffic networks.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"16 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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