Pub Date : 2024-03-03DOI: 10.1109/OJVT.2024.3396637
Olivia Nakayima;Mostafa I. Soliman;Kazunori Ueda;Samir A. Elsagheer Mohamed
Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.
{"title":"Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks","authors":"Olivia Nakayima;Mostafa I. Soliman;Kazunori Ueda;Samir A. Elsagheer Mohamed","doi":"10.1109/OJVT.2024.3396637","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3396637","url":null,"abstract":"Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10518068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects, including transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.
{"title":"Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges","authors":"Ruhul Amin Khalil;Ziad Safelnasr;Naod Yemane;Mebruk Kedir;Atawulrahman Shafiqurrahman;NASIR SAEED","doi":"10.1109/OJVT.2024.3369691","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3369691","url":null,"abstract":"Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects, including transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10444919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vehicle-to-everything (V2X) technology is pivotal for enhancing road safety, traffic efficiency, and energy conservation through the communication of vehicles with their surrounding entities such as other vehicles, pedestrians, roadside infrastructure, and networks. Among these, traffic signal control (TSC) plays a significant role in roadside infrastructure for V2X. However, most existing works on TSC design assume that real-time traffic flow information is accessible, which does not hold in real-world deployment. This study proposes a two-stage framework to address this issue. In the first stage, a scene prediction module and a scene context encoder are utilized to process historical and current traffic data to generate preliminary traffic signal actions. In the second stage, an action refinement module, informed by human-defined traffic rules and real-time traffic metrics, adjusts the preliminary actions to account for the latency in observations. This modular design allows device deployment with varying computational resources while facilitating system customization, ensuring both adaptability and scalability, particularly in edge-computing environments. Through extensive simulations on the SUMO platform, the proposed framework demonstrates robustness and superior performance in diverse traffic scenarios under varying communication delays. The related code is available at https://github.com/Traffic-Alpha/TSC-DelayLight .
{"title":"Scalable Reinforcement Learning Framework for Traffic Signal Control Under Communication Delays","authors":"Aoyu Pang;Maonan Wang;Yirong Chen;Man-On Pun;Michael Lepech","doi":"10.1109/OJVT.2024.3368693","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3368693","url":null,"abstract":"Vehicle-to-everything (V2X) technology is pivotal for enhancing road safety, traffic efficiency, and energy conservation through the communication of vehicles with their surrounding entities such as other vehicles, pedestrians, roadside infrastructure, and networks. Among these, traffic signal control (TSC) plays a significant role in roadside infrastructure for V2X. However, most existing works on TSC design assume that real-time traffic flow information is accessible, which does not hold in real-world deployment. This study proposes a two-stage framework to address this issue. In the first stage, a scene prediction module and a scene context encoder are utilized to process historical and current traffic data to generate preliminary traffic signal actions. In the second stage, an action refinement module, informed by human-defined traffic rules and real-time traffic metrics, adjusts the preliminary actions to account for the latency in observations. This modular design allows device deployment with varying computational resources while facilitating system customization, ensuring both adaptability and scalability, particularly in edge-computing environments. Through extensive simulations on the SUMO platform, the proposed framework demonstrates robustness and superior performance in diverse traffic scenarios under varying communication delays. The related code is available at \u0000<uri>https://github.com/Traffic-Alpha/TSC-DelayLight</uri>\u0000.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1109/OJVT.2024.3368240
Annu;P. Rajalakshmi
The advent of 6G marks a transformative phase in wireless communication, ushering in a hyperconnected experience. This paper explores optimizing sidelink technologies in Vehicle-to-Everything (V2X) communication through integrating 6G capabilities. Emphasizing challenges in sidelink resource allocation, the study introduces mathematical solutions. The survey further investigates the evolution of Cellular-V2X (C-V2X) and sidelink standardization within the 6G V2X Sidelink context, highlighting key features and applications. Additionally, it examines inter-UE coordination, resource re-evaluation, and pre-emption operations in 5G-V2X Sidelink, addressing associated challenges and mathematical formulations. The paper focuses on power-based resource allocation in 5G-V2X, addressing challenges and proposing solutions for the 6G V2X Sidelink landscape. Encompassing direct communication, collision issues, spectrum compliance, resource fairness, uncertainty, and interference management, the survey comprehensively explores challenges and solutions in current sidelink resource allocation. It evaluates traditional and emerging techniques, such as cognitive radio, cooperative communication, power control, dynamic spectrum access, ML-aided allocation, blockchain-enabled allocation, and edge computing-driven allocation. The resource allocation requirements for diverse V2X services in 6G V2X Sidelink are outlined, explicitly focusing on Vehicular to Vehicular (V2V), Vehicular to Infrastructure (V2I), Vehicular to Pedestrian (V2P), and other V2X services, addressing their specific needs.
{"title":"Towards 6G V2X Sidelink: Survey of Resource Allocation—Mathematical Formulations, Challenges, and Proposed Solutions","authors":"Annu;P. Rajalakshmi","doi":"10.1109/OJVT.2024.3368240","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3368240","url":null,"abstract":"The advent of 6G marks a transformative phase in wireless communication, ushering in a hyperconnected experience. This paper explores optimizing sidelink technologies in Vehicle-to-Everything (V2X) communication through integrating 6G capabilities. Emphasizing challenges in sidelink resource allocation, the study introduces mathematical solutions. The survey further investigates the evolution of Cellular-V2X (C-V2X) and sidelink standardization within the 6G V2X Sidelink context, highlighting key features and applications. Additionally, it examines inter-UE coordination, resource re-evaluation, and pre-emption operations in 5G-V2X Sidelink, addressing associated challenges and mathematical formulations. The paper focuses on power-based resource allocation in 5G-V2X, addressing challenges and proposing solutions for the 6G V2X Sidelink landscape. Encompassing direct communication, collision issues, spectrum compliance, resource fairness, uncertainty, and interference management, the survey comprehensively explores challenges and solutions in current sidelink resource allocation. It evaluates traditional and emerging techniques, such as cognitive radio, cooperative communication, power control, dynamic spectrum access, ML-aided allocation, blockchain-enabled allocation, and edge computing-driven allocation. The resource allocation requirements for diverse V2X services in 6G V2X Sidelink are outlined, explicitly focusing on Vehicular to Vehicular (V2V), Vehicular to Infrastructure (V2I), Vehicular to Pedestrian (V2P), and other V2X services, addressing their specific needs.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-16DOI: 10.1109/OJVT.2024.3366772
Hugo Hawkins;Chao Xu;Lie-Liang Yang;Lajos Hanzo
Index Modulated Orthogonal Frequency-Division Multiplexing (IM-OFDM) based Integrated Sensing and Communication (ISAC) is potentially capable of outperforming Orthogonal Frequency-Division Multiplexing (OFDM) ISAC, since Index Modulation (IM) concentrates increased power on the activated subcarriers. This has been confirmed by authoritative publications for the IM-OFDM communication component. However, no evidence is found in the open literature that IM-OFDM sensing is capable of outperforming OFDM sensing, because the blank subcarriers impair the system's sensing functionality. The existing solutions either insert a radar signal into the deactivated subcarriers, thereby using a radar signal for sensing, or employ compressed sensing, which leads to a lower sensing performance than OFDM ISAC. Hence, a novel low complexity algorithm is proposed for ensuring that an IM-OFDM ISAC system outperforms its OFDM ISAC counterpart for both communication and sensing. The algorithm collects observations of the received signal to “fill in” the blank subcarriers in the sensing data created by IM-OFDM, whilst taking advantage of the increased subcarrier power attained by activating fewer subcarriers. This occurs over multiple transmit frames, which inevitably delays the target estimation. As OFDM sensing assumes low target velocities, this delay is shown to have a negligible impact on the sensing performance of IM-OFDM. The simulation results show that IM-OFDM ISAC is indeed capable of outperforming its OFDM ISAC counterpart for both sensing and communication. The impact of block interleaving and of the modulation type on the sensing performance is also discussed.
{"title":"IM-OFDM ISAC Outperforms OFDM ISAC by Combining Multiple Sensing Observations","authors":"Hugo Hawkins;Chao Xu;Lie-Liang Yang;Lajos Hanzo","doi":"10.1109/OJVT.2024.3366772","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3366772","url":null,"abstract":"Index Modulated Orthogonal Frequency-Division Multiplexing (IM-OFDM) based Integrated Sensing and Communication (ISAC) is potentially capable of outperforming Orthogonal Frequency-Division Multiplexing (OFDM) ISAC, since Index Modulation (IM) concentrates increased power on the activated subcarriers. This has been confirmed by authoritative publications for the IM-OFDM communication component. However, no evidence is found in the open literature that IM-OFDM sensing is capable of outperforming OFDM sensing, because the blank subcarriers impair the system's sensing functionality. The existing solutions either insert a radar signal into the deactivated subcarriers, thereby using a radar signal for sensing, or employ compressed sensing, which leads to a lower sensing performance than OFDM ISAC. Hence, a novel low complexity algorithm is proposed for ensuring that an IM-OFDM ISAC system outperforms its OFDM ISAC counterpart for both communication and sensing. The algorithm collects observations of the received signal to “fill in” the blank subcarriers in the sensing data created by IM-OFDM, whilst taking advantage of the increased subcarrier power attained by activating fewer subcarriers. This occurs over multiple transmit frames, which inevitably delays the target estimation. As OFDM sensing assumes low target velocities, this delay is shown to have a negligible impact on the sensing performance of IM-OFDM. The simulation results show that IM-OFDM ISAC is indeed capable of outperforming its OFDM ISAC counterpart for both sensing and communication. The impact of block interleaving and of the modulation type on the sensing performance is also discussed.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10438844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140052853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1109/OJVT.2024.3358319
{"title":"IEEE Open Journal of Vehicular Technology Information for Authors","authors":"","doi":"10.1109/OJVT.2024.3358319","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3358319","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10436553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1109/OJVT.2024.3365531
Mohammad Taghi Dabiri;Mazen Hasna;Nizar Zorba;Tamer Khattab
In this article, our target is to design a permanent long backhaul link using unmanned aerial vehicle (UAV) relays and charge stations (CSs) to transfer data from the nearest core network to disaster area (DA). To this end, we first characterize the communication channel by considering the energy consumption models of the backup UAVs (moving UAVs) and the UAVs in service (hovering UAVs), the position and number of UAVs in service relative to the DA, along with the position of CSs relative to the position of UAVs. Then we define the optimization problem for two different scenarios. First, we design the long backhaul link in such a way that minimizes the implementation cost. In particular, the optimal design includes finding the optimal position for CSs, UAVs in service along with the optimal planning for backup UAVs in such a way as to reduce the implementation cost and guarantee the quality of service of the multi-relay UAV-based wireless backhaul links. The implementation cost is related to the number of CSs, the number of UAVs in service along with the number of backup UAVs. For the second scenario, we assume that the implementation cost is predetermined, and we find the optimal positions for UAVs and CSs along with planning for backup UAVs to minimize the outage probability. By analyzing the effects of optimization parameters, we further propose low complexity sub-optimal algorithms for both scenarios. Then, using simulations, we show that the sub-optimal algorithms achieve a performance that is very close to the optimal solutions.
{"title":"Enabling Flexible Arial Backhaul Links for Post Disasters: A Design Using UAV Swarms and Distributed Charging Stations","authors":"Mohammad Taghi Dabiri;Mazen Hasna;Nizar Zorba;Tamer Khattab","doi":"10.1109/OJVT.2024.3365531","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3365531","url":null,"abstract":"In this article, our target is to design a permanent long backhaul link using unmanned aerial vehicle (UAV) relays and charge stations (CSs) to transfer data from the nearest core network to disaster area (DA). To this end, we first characterize the communication channel by considering the energy consumption models of the backup UAVs (moving UAVs) and the UAVs in service (hovering UAVs), the position and number of UAVs in service relative to the DA, along with the position of CSs relative to the position of UAVs. Then we define the optimization problem for two different scenarios. First, we design the long backhaul link in such a way that minimizes the implementation cost. In particular, the optimal design includes finding the optimal position for CSs, UAVs in service along with the optimal planning for backup UAVs in such a way as to reduce the implementation cost and guarantee the quality of service of the multi-relay UAV-based wireless backhaul links. The implementation cost is related to the number of CSs, the number of UAVs in service along with the number of backup UAVs. For the second scenario, we assume that the implementation cost is predetermined, and we find the optimal positions for UAVs and CSs along with planning for backup UAVs to minimize the outage probability. By analyzing the effects of optimization parameters, we further propose low complexity sub-optimal algorithms for both scenarios. Then, using simulations, we show that the sub-optimal algorithms achieve a performance that is very close to the optimal solutions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433686","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140161142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1109/OJVT.2024.3358317
{"title":"IEEE Vehicular Technology Society IEEE Open Journal on Vehicular Technology Information","authors":"","doi":"10.1109/OJVT.2024.3358317","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3358317","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10431554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Location-based services find a number of applications in vehicular environments such as navigation, parking, infortainment etc. However, the disclosure of vehicles' location information raises multiple privacy issues. To balance the tradeoff between privacy and utility, this paper proposes a framework to preserve users' location privacy while delivering the desired quality of experience (QoE). The proposed framework allows users to quantify the data utility while accessing location-based services under different privacy levels through the QoE metric. The privacy analysis of the proposed framework is provided under two adversary models. Finally, the effectiveness of the proposed framework is demonstrate using the real-world “Dianping” review dataset.
{"title":"A Framework for Tradeoff Between Location Privacy Preservation and Quality of Experience in Location Based Services","authors":"Tianyi Feng;Zhixiang Zhang;Wai-Choong Wong;Sumei Sun;Biplab Sikdar","doi":"10.1109/OJVT.2024.3364184","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3364184","url":null,"abstract":"Location-based services find a number of applications in vehicular environments such as navigation, parking, infortainment etc. However, the disclosure of vehicles' location information raises multiple privacy issues. To balance the tradeoff between privacy and utility, this paper proposes a framework to preserve users' location privacy while delivering the desired quality of experience (QoE). The proposed framework allows users to quantify the data utility while accessing location-based services under different privacy levels through the QoE metric. The privacy analysis of the proposed framework is provided under two adversary models. Finally, the effectiveness of the proposed framework is demonstrate using the real-world “Dianping” review dataset.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10430221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140291202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-09DOI: 10.1109/OJVT.2024.3358321
{"title":"IEEE Vehicular Technology Society Information","authors":"","doi":"10.1109/OJVT.2024.3358321","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3358321","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10431555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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