Pub Date : 2025-04-10DOI: 10.1016/j.vehcom.2025.100917
Yin Wang, Kang'An Gui
This article investigates the joint optimization of task offloading and computation scheduling in low Earth orbit (LEO) satellite networks, where each LEO satellite is equipped with a reconfigurable intelligent surface (RIS). By considering the inherent characteristics of tasks and the energy consumption associated with task execution, we define a system utility function and formulate the problem as a constrained utility maximization problem. To address this optimization challenge, we first propose a priority-based task offloading and computation scheduling strategy tailored for single-satellite execution scenarios. Subsequently, we extend this approach to multi-satellite collaborative task execution scenarios, where a knapsack algorithm-based strategy is developed to optimize task allocation and scheduling. To underscore the advantages of the proposed RIS-assisted multi-satellite framework, we introduce a comparative analysis with a non-RIS-assisted multi-satellite offloading mode. Extensive simulations conducted in Satellite Tool Kit (STK) and MATLAB demonstrate that the RIS-assisted multi-satellite mode significantly outperforms its non-RIS counterpart in terms of system utility and energy efficiency. The results validate the effectiveness of the proposed algorithms and highlight the potential of RIS technology in enhancing the performance of LEO satellite networks.
{"title":"Task offloading and computational scheduling in RIS-assisted low Earth orbit satellite communication networks","authors":"Yin Wang, Kang'An Gui","doi":"10.1016/j.vehcom.2025.100917","DOIUrl":"10.1016/j.vehcom.2025.100917","url":null,"abstract":"<div><div>This article investigates the joint optimization of task offloading and computation scheduling in low Earth orbit (LEO) satellite networks, where each LEO satellite is equipped with a reconfigurable intelligent surface (RIS). By considering the inherent characteristics of tasks and the energy consumption associated with task execution, we define a system utility function and formulate the problem as a constrained utility maximization problem. To address this optimization challenge, we first propose a priority-based task offloading and computation scheduling strategy tailored for single-satellite execution scenarios. Subsequently, we extend this approach to multi-satellite collaborative task execution scenarios, where a knapsack algorithm-based strategy is developed to optimize task allocation and scheduling. To underscore the advantages of the proposed RIS-assisted multi-satellite framework, we introduce a comparative analysis with a non-RIS-assisted multi-satellite offloading mode. Extensive simulations conducted in Satellite Tool Kit (STK) and MATLAB demonstrate that the RIS-assisted multi-satellite mode significantly outperforms its non-RIS counterpart in terms of system utility and energy efficiency. The results validate the effectiveness of the proposed algorithms and highlight the potential of RIS technology in enhancing the performance of LEO satellite networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100917"},"PeriodicalIF":5.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828419","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 : 2025-04-10DOI: 10.1016/j.vehcom.2025.100918
Jamile Khalili Shahrouz, Morteza Analoui
In recent years, Vehicular Ad-hoc Networks (VANETs) have gained widespread acceptance to enable vehicles to communicate and exchange critical information, such as road conditions, traffic congestion, speed, and vehicle locations. Nonetheless, the wireless nature of VANET communication renders it susceptible to various security attacks. To counter these vulnerabilities, privacy-preserving authentication schemes play a crucial role. Many of these schemes rely on Public Key Infrastructure (PKI) to mitigate security risks, ensuring authentication and message integrity through public key certificates. However, a significant drawback of these schemes arises from utilizing the Certificate Revocation List (CRL), which introduces notable delays. In addition, vehicular networks are time-sensitive, and prolonged delays may lead to severe consequences. Moreover, CRL checking can inadvertently leak sensitive vehicle information, making PKI-based schemes impractical for VANETs. To address these challenges, we propose a privacy-preserving revocation mechanism based on a zero-knowledge accumulator, tailored specifically for VANETs. This mechanism significantly reduces the time spent checking revoked certificates while preserving user privacy. Furthermore, by utilizing Blockchain technology to publish revoked certificates in a distributed manner, we can reduce the time required for distribution, decrease network congestion, improve efficiency, and eliminate reliance on a single central authority. Additionally, our approach aims to overcome the issue of non-membership witness updates in batch mode. Our proposed privacy-aware scheme has been rigorously evaluated using the automatic verification tool ProVerif. The results confirm that our solution guarantees the desired properties of anonymity and unlinkability. Through extensive simulation and performance analysis, we demonstrate that our scheme is not only privacy-preserving but also an efficient and practical solution for real-world deployment in VANETs.
{"title":"Privacy-aware revocation in VANETs with a Blockchain using accumulator","authors":"Jamile Khalili Shahrouz, Morteza Analoui","doi":"10.1016/j.vehcom.2025.100918","DOIUrl":"10.1016/j.vehcom.2025.100918","url":null,"abstract":"<div><div>In recent years, Vehicular Ad-hoc Networks (VANETs) have gained widespread acceptance to enable vehicles to communicate and exchange critical information, such as road conditions, traffic congestion, speed, and vehicle locations. Nonetheless, the wireless nature of VANET communication renders it susceptible to various security attacks. To counter these vulnerabilities, privacy-preserving authentication schemes play a crucial role. Many of these schemes rely on Public Key Infrastructure (PKI) to mitigate security risks, ensuring authentication and message integrity through public key certificates. However, a significant drawback of these schemes arises from utilizing the Certificate Revocation List (CRL), which introduces notable delays. In addition, vehicular networks are time-sensitive, and prolonged delays may lead to severe consequences. Moreover, CRL checking can inadvertently leak sensitive vehicle information, making PKI-based schemes impractical for VANETs. To address these challenges, we propose a privacy-preserving revocation mechanism based on a zero-knowledge accumulator, tailored specifically for VANETs. This mechanism significantly reduces the time spent checking revoked certificates while preserving user privacy. Furthermore, by utilizing Blockchain technology to publish revoked certificates in a distributed manner, we can reduce the time required for distribution, decrease network congestion, improve efficiency, and eliminate reliance on a single central authority. Additionally, our approach aims to overcome the issue of non-membership witness updates in batch mode. Our proposed privacy-aware scheme has been rigorously evaluated using the automatic verification tool ProVerif. The results confirm that our solution guarantees the desired properties of anonymity and unlinkability. Through extensive simulation and performance analysis, we demonstrate that our scheme is not only privacy-preserving but also an efficient and practical solution for real-world deployment in VANETs.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100918"},"PeriodicalIF":5.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828366","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 : 2025-04-09DOI: 10.1016/j.vehcom.2025.100919
Ming Sun , Zexu Jiang , Erhan Dong , Tianyu Lv
Vehicular networking plays an indispensable role in enhancing road safety and traffic efficiency. Although existing technologies have made significant progress in reusing vehicle-to-infrastructure (V2I) link resources for vehicle-to-vehicle (V2V) links, they still face challenges such as the high dimensionality of the joint action space and unsatisfactory optimization with limited in-vehicle radio resources, variable environments, and uncertainties. Reinforcement learning-based joint algorithms that separately optimize channel allocation and power selection can reduce the dimensionality of the joint action space. However, it is difficult to effectively coordinate channel allocation and power selection, which greatly affects the performance of them. To address these challenges, this paper proposes a distributed multi-agent joint optimization algorithm based on a novel cross-entropy loss-based reinforcement learning (CERL) algorithm and the A2C algorithm for separately optimizing channels and power in vehicular networks. Furthermore, a multi-round stochastic search strategy is presented to optimize the experience pools and coordinate the channel allocation and the power selection for the proposed distributed multi-agent joint optimization algorithm. With the help of the multi-round stochastic search strategy, the proposed distributed multi-agent joint optimization algorithm can significantly improve the optimization performance in resource allocation. To evaluate the performance of the proposed distributed multi-agent joint optimization algorithm in both the V2V link transmission success rate and the V2I link throughput, a comprehensive simulation study is conducted under different channel resource availability scenarios with different sizes of security data. The experimental results demonstrate that our proposed algorithm can significantly improve the V2I link throughput and the V2V link transmission success rate, and outperforms the existing algorithms in terms of radio efficiency. Specifically, under two different channel resource availability scenarios, our proposed algorithm can achieve more than 99.9 % average V2V link transmission success rate and 2.99 Mbps and 2.07 Mbps higher average V2I link throughput than the competitive algorithm D3QN-LS when the security data size ranges from 1 × 1060 Bytes to 8 × 1060 Bytes. The proposed algorithm theoretically provides a new perspective and solution for separately optimizing channels and power in high-dimensional complex dynamic environments of vehicular networks.
{"title":"A distributed multi-agent joint optimization algorithm based on CERL and A2C for resource allocation in vehicular networks","authors":"Ming Sun , Zexu Jiang , Erhan Dong , Tianyu Lv","doi":"10.1016/j.vehcom.2025.100919","DOIUrl":"10.1016/j.vehcom.2025.100919","url":null,"abstract":"<div><div>Vehicular networking plays an indispensable role in enhancing road safety and traffic efficiency. Although existing technologies have made significant progress in reusing vehicle-to-infrastructure (V2I) link resources for vehicle-to-vehicle (V2V) links, they still face challenges such as the high dimensionality of the joint action space and unsatisfactory optimization with limited in-vehicle radio resources, variable environments, and uncertainties. Reinforcement learning-based joint algorithms that separately optimize channel allocation and power selection can reduce the dimensionality of the joint action space. However, it is difficult to effectively coordinate channel allocation and power selection, which greatly affects the performance of them. To address these challenges, this paper proposes a distributed multi-agent joint optimization algorithm based on a novel cross-entropy loss-based reinforcement learning (CERL) algorithm and the A2C algorithm for separately optimizing channels and power in vehicular networks. Furthermore, a multi-round stochastic search strategy is presented to optimize the experience pools and coordinate the channel allocation and the power selection for the proposed distributed multi-agent joint optimization algorithm. With the help of the multi-round stochastic search strategy, the proposed distributed multi-agent joint optimization algorithm can significantly improve the optimization performance in resource allocation. To evaluate the performance of the proposed distributed multi-agent joint optimization algorithm in both the V2V link transmission success rate and the V2I link throughput, a comprehensive simulation study is conducted under different channel resource availability scenarios with different sizes of security data. The experimental results demonstrate that our proposed algorithm can significantly improve the V2I link throughput and the V2V link transmission success rate, and outperforms the existing algorithms in terms of radio efficiency. Specifically, under two different channel resource availability scenarios, our proposed algorithm can achieve more than 99.9 % average V2V link transmission success rate and 2.99 Mbps and 2.07 Mbps higher average V2I link throughput than the competitive algorithm D3QN-LS when the security data size ranges from 1 × 1060 Bytes to 8 × 1060 Bytes. The proposed algorithm theoretically provides a new perspective and solution for separately optimizing channels and power in high-dimensional complex dynamic environments of vehicular networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100919"},"PeriodicalIF":5.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825647","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 : 2025-04-04DOI: 10.1016/j.vehcom.2025.100905
Amir Masoud Rahmani , Amir Haider , Monji Mohamed Zaidi , Abed Alanazi , Shtwai Alsubai , Abdullah Alqahtani , Mohammad Sadegh Yousefpoor , Efat Yousefpoor , Mehdi Hosseinzadeh
To ensure reliable data transmission in flying ad hoc networks (FANETs), efficient routing protocols are necessary to establish communication paths in FANETs. Recently, reinforcement learning (RL), particularly Q-learning, has become a promising approach for overcoming challenges faced by traditional routing protocols due to its capacity for autonomous adaptation and self-learning. This study presents a Q-learning-based routing strategy, enhanced by an innovative cylindrical filtering technique, named QRCF in FANETs. In QRCF, the dissemination interval of hello packets is adaptively adjusted based on the connection status of nearby UAVs. Then, this routing process leverages Q-learning to discover reliable and stable routes, using a state set refined by the cylindrical filtering technique to accelerate the search for the optimal path in the network. Afterward, the reward value is computed using metrics such as relative speed, connection time, residual energy, and movement path. Finally, QRCF is deployed in the network simulator 2 (NS2), and its performance is evaluated against three routing schemes, QRF, QFAN, and QTAR. These evaluations are presented based on the number of UAVs and their speed. In general, when changing the number of nodes, QRCF improves energy usage (about 5.01%), data delivery ratio (approximately 1.20%), delay (17.71%), and network longevity (about 3.21%). However, it has a higher overhead (approximately 10.91%) than QRF. Moreover, when changing the speed of UAVs in the network, QRCF improves energy usage (about 4.94%), data delivery ratio (approximately 2.36%), delay (about 17.5%), and network lifetime (approximately 8.75%). However, it increases routing overhead (approximately 15.47%) in comparison with QRF.
{"title":"QRCF: A new Q-learning-based routing approach using a smart cylindrical filtering system in flying ad hoc networks","authors":"Amir Masoud Rahmani , Amir Haider , Monji Mohamed Zaidi , Abed Alanazi , Shtwai Alsubai , Abdullah Alqahtani , Mohammad Sadegh Yousefpoor , Efat Yousefpoor , Mehdi Hosseinzadeh","doi":"10.1016/j.vehcom.2025.100905","DOIUrl":"10.1016/j.vehcom.2025.100905","url":null,"abstract":"<div><div>To ensure reliable data transmission in flying ad hoc networks (FANETs), efficient routing protocols are necessary to establish communication paths in FANETs. Recently, reinforcement learning (RL), particularly Q-learning, has become a promising approach for overcoming challenges faced by traditional routing protocols due to its capacity for autonomous adaptation and self-learning. This study presents a Q-learning-based routing strategy, enhanced by an innovative cylindrical filtering technique, named QRCF in FANETs. In QRCF, the dissemination interval of hello packets is adaptively adjusted based on the connection status of nearby UAVs. Then, this routing process leverages Q-learning to discover reliable and stable routes, using a state set refined by the cylindrical filtering technique to accelerate the search for the optimal path in the network. Afterward, the reward value is computed using metrics such as relative speed, connection time, residual energy, and movement path. Finally, QRCF is deployed in the network simulator 2 (NS2), and its performance is evaluated against three routing schemes, QRF, QFAN, and QTAR. These evaluations are presented based on the number of UAVs and their speed. In general, when changing the number of nodes, QRCF improves energy usage (about 5.01%), data delivery ratio (approximately 1.20%), delay (17.71%), and network longevity (about 3.21%). However, it has a higher overhead (approximately 10.91%) than QRF. Moreover, when changing the speed of UAVs in the network, QRCF improves energy usage (about 4.94%), data delivery ratio (approximately 2.36%), delay (about 17.5%), and network lifetime (approximately 8.75%). However, it increases routing overhead (approximately 15.47%) in comparison with QRF.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100905"},"PeriodicalIF":5.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792512","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 : 2025-04-04DOI: 10.1016/j.vehcom.2025.100915
Yonghua Xiong , Yan Zhou , Jinhua She , Anjun Yu
The suddenness of natural disasters demands rapid response and timely information. The rapid development of unmanned aerial vehicle (UAV) technology offers new opportunities for post-disaster assessment. At the same time, UAV swarms covering multiple post-disaster regions also face challenges. Uneven UAV utilization and region allocation can lead to overuse and excessive energy consumption of certain UAVs, reducing collaboration effectiveness and coverage efficiency. To improve the collaboration efficiency, we present a metric for collaboration synchronization rate, rationally allocate regions, and optimize coverage paths to reduce the travel distance difference between UAVs. Minimizing the number of UAVs used and shortening the total travel distance can improve the coverage efficiency. In this paper, we study the Multi-UAV Multi-region Complete Coverage Path Planning (MMCCPP) problem in post-disaster scenarios. First, we establish a multi-objective model that optimizes the number of UAVs, total travel distance, and collaboration synchronization rate. Then, we develop the Coverage Path Planning (SPSO-CPP) method based on improved Set-Based Particle Swarm Optimization (S-PSO) to plan the minimum number of UAVs and the optimal coverage paths, incorporating a greedy region-chosen mechanism and comprehensive optimization of paths within and between regions. Finally, we validate the feasibility, effectiveness, and superiority of the proposed algorithm through simulation test comparisons.
{"title":"Collaborative coverage path planning for UAV swarm for multi-region post-disaster assessment","authors":"Yonghua Xiong , Yan Zhou , Jinhua She , Anjun Yu","doi":"10.1016/j.vehcom.2025.100915","DOIUrl":"10.1016/j.vehcom.2025.100915","url":null,"abstract":"<div><div>The suddenness of natural disasters demands rapid response and timely information. The rapid development of unmanned aerial vehicle (UAV) technology offers new opportunities for post-disaster assessment. At the same time, UAV swarms covering multiple post-disaster regions also face challenges. Uneven UAV utilization and region allocation can lead to overuse and excessive energy consumption of certain UAVs, reducing collaboration effectiveness and coverage efficiency. To improve the collaboration efficiency, we present a metric for collaboration synchronization rate, rationally allocate regions, and optimize coverage paths to reduce the travel distance difference between UAVs. Minimizing the number of UAVs used and shortening the total travel distance can improve the coverage efficiency. In this paper, we study the Multi-UAV Multi-region Complete Coverage Path Planning (MMCCPP) problem in post-disaster scenarios. First, we establish a multi-objective model that optimizes the number of UAVs, total travel distance, and collaboration synchronization rate. Then, we develop the Coverage Path Planning (SPSO-CPP) method based on improved Set-Based Particle Swarm Optimization (S-PSO) to plan the minimum number of UAVs and the optimal coverage paths, incorporating a greedy region-chosen mechanism and comprehensive optimization of paths within and between regions. Finally, we validate the feasibility, effectiveness, and superiority of the proposed algorithm through simulation test comparisons.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100915"},"PeriodicalIF":5.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792510","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 : 2025-03-24DOI: 10.1016/j.vehcom.2025.100913
Parisa Khoshvaght , Jawad Tanveer , Amir Masoud Rahmani , May Altulyan , Yazeed Alkhrijah , Mohammad Sadegh Yousefpoor , Efat Yousefpoor , Mokhtar Mohammadi , Mehdi Hosseinzadeh
Recently, the rapid development of wireless technologies, low-priced equipment, advances in networking protocols, and access to modern communication, electrical, and sensing technologies have led to the evolution of flying ad hoc networks (FANETs). However, the high movement of unmanned aerial vehicles (UAVs) in these networks causes iterated failures of communication links and constant changes in network topology. These features challenge the design of a proper routing protocol in FANETs. Today, computational intelligence (CI) techniques are rapidly developing as a mighty and intelligent computing model. This promising technology can be used to improve various applied areas, especially routing in FANETs. This paper examines and assesses various CI-based routing techniques in FANETs. Accordingly, this paper introduces a classification of CI-based routing protocols for FANETs. This categorization includes three groups: learning system-based routing methods (including artificial neural networks, reinforcement learning, and deep reinforcement learning), fuzzy-based routing schemes, and bio-inspired routing schemes (evolutionary algorithms and swarm intelligence). Subsequently, based on the offered classification, the most recent CI-based routing methods and their key features are outlined. Ultimately, the opportunities and challenges in this area have been mentioned to help researchers familiarize themselves with future research directions in CI-based routing algorithms for FANETs and work toward improving these methods in such networks.
{"title":"Computational intelligence-based routing schemes in flying ad-hoc networks (FANETs): A review","authors":"Parisa Khoshvaght , Jawad Tanveer , Amir Masoud Rahmani , May Altulyan , Yazeed Alkhrijah , Mohammad Sadegh Yousefpoor , Efat Yousefpoor , Mokhtar Mohammadi , Mehdi Hosseinzadeh","doi":"10.1016/j.vehcom.2025.100913","DOIUrl":"10.1016/j.vehcom.2025.100913","url":null,"abstract":"<div><div>Recently, the rapid development of wireless technologies, low-priced equipment, advances in networking protocols, and access to modern communication, electrical, and sensing technologies have led to the evolution of flying ad hoc networks (FANETs). However, the high movement of unmanned aerial vehicles (UAVs) in these networks causes iterated failures of communication links and constant changes in network topology. These features challenge the design of a proper routing protocol in FANETs. Today, computational intelligence (CI) techniques are rapidly developing as a mighty and intelligent computing model. This promising technology can be used to improve various applied areas, especially routing in FANETs. This paper examines and assesses various CI-based routing techniques in FANETs. Accordingly, this paper introduces a classification of CI-based routing protocols for FANETs. This categorization includes three groups: learning system-based routing methods (including artificial neural networks, reinforcement learning, and deep reinforcement learning), fuzzy-based routing schemes, and bio-inspired routing schemes (evolutionary algorithms and swarm intelligence). Subsequently, based on the offered classification, the most recent CI-based routing methods and their key features are outlined. Ultimately, the opportunities and challenges in this area have been mentioned to help researchers familiarize themselves with future research directions in CI-based routing algorithms for FANETs and work toward improving these methods in such networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100913"},"PeriodicalIF":5.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725010","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 : 2025-03-20DOI: 10.1016/j.vehcom.2025.100914
Mohammed A. Alhartomi , M.F.L. Abdullah , Wafi A.B. Mabrouk , Ahmed Alzahmi , Saeed Alzahrani
This paper investigates the potential of Free Space Optical (FSO) communication technology for high-speed train (HST) systems by developing mathematical models for G2T-FSO (Ground to Train) communication links across single, curved, and double-curved tracks. The key contribution of this research is the introduction of novel G2T-FSO models, incorporating multiple transmitters (single, double, triple, and quad) and considering different weather conditions (clear, rain, and fog) using NRZ-OOK modulation. The models were evaluated based on key performance metrics, including received power, signal-to-noise ratio (SNR), bit error rate (BER), and eye diagrams. Simulation results reveal that single and dual transmitter links are significantly impacted by geometrical and atmospheric losses, while triple and quad transmitters provide error-free G2T-FSO links with a BER of 10-9. Under clear weather conditions, communication ranges of up to 680 meters for straight tracks and 618 meters for curved tracks were achieved. These findings highlight that G2T-FSO links deliver superior performance compared to traditional HST communication technologies, offering enhanced range, reliability, and data capacity for high-speed, secure train communication systems.
{"title":"Modelling, optimisation and evaluation of multi-transmitters FSO link for ground to train communication","authors":"Mohammed A. Alhartomi , M.F.L. Abdullah , Wafi A.B. Mabrouk , Ahmed Alzahmi , Saeed Alzahrani","doi":"10.1016/j.vehcom.2025.100914","DOIUrl":"10.1016/j.vehcom.2025.100914","url":null,"abstract":"<div><div>This paper investigates the potential of Free Space Optical (FSO) communication technology for high-speed train (HST) systems by developing mathematical models for G2T-FSO (Ground to Train) communication links across single, curved, and double-curved tracks. The key contribution of this research is the introduction of novel G2T-FSO models, incorporating multiple transmitters (single, double, triple, and quad) and considering different weather conditions (clear, rain, and fog) using NRZ-OOK modulation. The models were evaluated based on key performance metrics, including received power, signal-to-noise ratio (SNR), bit error rate (BER), and eye diagrams. Simulation results reveal that single and dual transmitter links are significantly impacted by geometrical and atmospheric losses, while triple and quad transmitters provide error-free G2T-FSO links with a BER of 10<sup>-9</sup>. Under clear weather conditions, communication ranges of up to 680 meters for straight tracks and 618 meters for curved tracks were achieved. These findings highlight that G2T-FSO links deliver superior performance compared to traditional HST communication technologies, offering enhanced range, reliability, and data capacity for high-speed, secure train communication systems.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100914"},"PeriodicalIF":5.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725011","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 : 2025-03-13DOI: 10.1016/j.vehcom.2025.100911
Md Asif Thanedar , Sanjaya Kumar Panda
The delay-sensitive applications, such as self-driving, smart transportation, navigation, and augmented reality assistance, can be evolved in vehicular ad-hoc networks (VANETs) using one of the leading paradigms, fog computing (FC). The intelligent vehicles are connected to the roadside infrastructure, such as high power nodes (HPNs) and roadside units (RSUs), also called fog nodes (FNs), for obtaining on-demand services. These FNs possess finite resources and can provide services to limited vehicles. However, when vehicles reach the network spike in demand, the FNs become impuissant in furnishing services in the existing solutions. As a result, there is a significant reduction in the network throughput. Therefore, we propose an efficient resource orchestration (ERO) algorithm to maximize the throughput by reducing the allocated resource blocks (RBs) of FNs. The ERO algorithm partitions the FN coverage region into restricted and non-restricted coverage regions. Then, it coordinates the RBs allocation among FNs by reducing RBs for the vehicles in the non-restricted coverage regions. This reduction is carried out by migrating RBs for offloading upstream services so that the overall occupied capacity of FNs is minimized. ERO constructs the minimum priority queue using the occupied capacity of FNs to perform optimal RBs migration between pairs of FNs. The ERO algorithm is evaluated, and simulation results show that the proposed algorithm performs better in terms of throughput, serviceability, availability, and service capability than existing algorithms.
{"title":"An efficient resource orchestration algorithm for enhancing throughput in fog computing-enabled vehicular networks","authors":"Md Asif Thanedar , Sanjaya Kumar Panda","doi":"10.1016/j.vehcom.2025.100911","DOIUrl":"10.1016/j.vehcom.2025.100911","url":null,"abstract":"<div><div>The delay-sensitive applications, such as self-driving, smart transportation, navigation, and augmented reality assistance, can be evolved in vehicular ad-hoc networks (VANETs) using one of the leading paradigms, fog computing (FC). The intelligent vehicles are connected to the roadside infrastructure, such as high power nodes (HPNs) and roadside units (RSUs), also called fog nodes (FNs), for obtaining on-demand services. These FNs possess finite resources and can provide services to limited vehicles. However, when vehicles reach the network spike in demand, the FNs become impuissant in furnishing services in the existing solutions. As a result, there is a significant reduction in the network throughput. Therefore, we propose an efficient resource orchestration (ERO) algorithm to maximize the throughput by reducing the allocated resource blocks (RBs) of FNs. The ERO algorithm partitions the FN coverage region into restricted and non-restricted coverage regions. Then, it coordinates the RBs allocation among FNs by reducing RBs for the vehicles in the non-restricted coverage regions. This reduction is carried out by migrating RBs for offloading upstream services so that the overall occupied capacity of FNs is minimized. ERO constructs the minimum priority queue using the occupied capacity of FNs to perform optimal RBs migration between pairs of FNs. The ERO algorithm is evaluated, and simulation results show that the proposed algorithm performs better in terms of throughput, serviceability, availability, and service capability than existing algorithms.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100911"},"PeriodicalIF":5.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621242","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 : 2025-03-13DOI: 10.1016/j.vehcom.2025.100908
Yiyang Zhang , Yuan Yin , Jiaheng Wang , Kang Zheng
Non-orthogonal multiple access (NOMA) has recently been integrated into beamspace multiple-input multiple-output (MIMO) for improved capacity and throughput. To apply the promising beamspace MIMO-NOMA in practical millimeter-wave applications, finite blocklength has to be considered. Therefore, in this article, we consider the effective throughput as the performance metric, which incorporates both the transmission rate and error performance in the finite blocklength regime. For the considered downlink beamspace MIMO-NOMA system, we derive the expression of system effective throughput with given blocklength and decoding error probability. To maximize the system effective throughput and simultaneously provide the quality-of-service (QoS) guarantee of data rate for each user, the transmit precoding and power allocation are optimized. We first provide an effective precoding design to mitigate the inter-beam interference. For power allocation, we apply monotonic optimization to obtain a globally optimal solution, and further develop a low-complexity algorithm based on the principles of convex-concave procedure (CCP). Simulation results show that the proposed schemes achieve higher spectrum and energy efficiency compared to several baseline schemes, including the traditional resource allocation algorithm based on the infinite blocklength assumption, and the existing beamspace MIMO.
{"title":"Effective throughput maximization of beamspace MIMO-NOMA with finite blocklength","authors":"Yiyang Zhang , Yuan Yin , Jiaheng Wang , Kang Zheng","doi":"10.1016/j.vehcom.2025.100908","DOIUrl":"10.1016/j.vehcom.2025.100908","url":null,"abstract":"<div><div>Non-orthogonal multiple access (NOMA) has recently been integrated into beamspace multiple-input multiple-output (MIMO) for improved capacity and throughput. To apply the promising beamspace MIMO-NOMA in practical millimeter-wave applications, finite blocklength has to be considered. Therefore, in this article, we consider the effective throughput as the performance metric, which incorporates both the transmission rate and error performance in the finite blocklength regime. For the considered downlink beamspace MIMO-NOMA system, we derive the expression of system effective throughput with given blocklength and decoding error probability. To maximize the system effective throughput and simultaneously provide the quality-of-service (QoS) guarantee of data rate for each user, the transmit precoding and power allocation are optimized. We first provide an effective precoding design to mitigate the inter-beam interference. For power allocation, we apply monotonic optimization to obtain a globally optimal solution, and further develop a low-complexity algorithm based on the principles of convex-concave procedure (CCP). Simulation results show that the proposed schemes achieve higher spectrum and energy efficiency compared to several baseline schemes, including the traditional resource allocation algorithm based on the infinite blocklength assumption, and the existing beamspace MIMO.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100908"},"PeriodicalIF":5.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642083","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 : 2025-03-13DOI: 10.1016/j.vehcom.2025.100910
Jiazheng Lv, Jianhua Cheng, Peng Li, Runze Bai
This paper considers a mobile jammer-aided unmanned aerial vehicle (UAV) relay communication system, where a relay UAV assists information transmission between the source node and the destination node, while a friendly jammer UAV emits an interference signal to the eavesdropper to suppress its eavesdropping behavior. The secure energy efficiency (SEE) maximization problem is studied. The objective is to maximize the SEE via jointly optimizing power and UAVs' trajectories. The formulated problem is non-convex and subject to information-causality constraints, power constraints, and mobility constraints, which cannot be solved directly by convex optimization tools. To solve the problem, the block coordinate descent method is applied to decouple the original problem into four sub-problems. Then, an efficient iterative algorithm is proposed to address the non-convex problem through the successive convex approximation technique. Additionally, Dinkelbach's algorithm is employed to handle the fractional programming problem, thereby obtaining an approximate solution with guaranteed convergence. Different schemes are evaluated to validate the effectiveness of the proposed design. The simulation results show that the proposed design can improve SEE effectively compared with other schemes.
{"title":"Secure energy efficiency maximization for mobile jammer-aided UAV communication: Joint power and trajectory optimization","authors":"Jiazheng Lv, Jianhua Cheng, Peng Li, Runze Bai","doi":"10.1016/j.vehcom.2025.100910","DOIUrl":"10.1016/j.vehcom.2025.100910","url":null,"abstract":"<div><div>This paper considers a mobile jammer-aided unmanned aerial vehicle (UAV) relay communication system, where a relay UAV assists information transmission between the source node and the destination node, while a friendly jammer UAV emits an interference signal to the eavesdropper to suppress its eavesdropping behavior. The secure energy efficiency (SEE) maximization problem is studied. The objective is to maximize the SEE via jointly optimizing power and UAVs' trajectories. The formulated problem is non-convex and subject to information-causality constraints, power constraints, and mobility constraints, which cannot be solved directly by convex optimization tools. To solve the problem, the block coordinate descent method is applied to decouple the original problem into four sub-problems. Then, an efficient iterative algorithm is proposed to address the non-convex problem through the successive convex approximation technique. Additionally, Dinkelbach's algorithm is employed to handle the fractional programming problem, thereby obtaining an approximate solution with guaranteed convergence. Different schemes are evaluated to validate the effectiveness of the proposed design. The simulation results show that the proposed design can improve SEE effectively compared with other schemes.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"53 ","pages":"Article 100910"},"PeriodicalIF":5.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643796","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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