The Experts below are selected from a list of 398205 Experts worldwide ranked by ideXlab platform
Ming Liu - One of the best experts on this subject based on the ideXlab platform.
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VPOD: virtual parking overlay network based Data Delivery in urban VANETs
International Journal of Ad Hoc and Ubiquitous Computing, 2015Co-Authors: Jinqi Zhu, Nianbo Liu, Ming Liu, Weijia FengAbstract:Vehicular ad hoc networks (VANETs) have characteristics of intermittent connectivity, high mobility of vehicle nodes and dynamic topology, which make Data Delivery in VANETs very challenging. Pervious works that based on history traffic patterns to predict the current traffic conditions on the roads are not accurate. Moreover, deploying roadside units (RSUs) is a possible solution to overcome the challenges, but it often requires a large amount of investment. Motivated by the fact that there are large amounts of outside parked vehicles in urban areas, we propose a virtual parking overlay network based Data Delivery scheme (VPOD), which does not need any RSUs but leverages a parking overlay network formed by outside parked vehicles to disseminate messages among moving vehicles. Simulation results based on a real city map and realistic traffic situations show that VPOD achieves high performance in Data Delivery, especially in sparse traffic and multiple requests conditions.
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GLOBECOM - Red or green: Analyzing the Data Delivery with traffic lights in vehicular ad hoc networks
2014 IEEE Global Communications Conference, 2014Co-Authors: Chao Song, Wei-shih Yang, Ming LiuAbstract:The Data Delivery in Vehicular Ad Hoc Networks (VANETs) depends on the mobility of the vehicles (e.g. with carry-and-forward). However, the mobility of the vehicles is not only affected by the nodes themselves, but also by some external means such as the traffic lights. The red light stops the vehicles at the intersection, which will increase the Delivery delay of the messages carried by the vehicle with waiting time. On the contrary, this may also increase the opportunities of vehicles moving behind to catch up in forwarding messages. In this paper, we investigate the negative and positive influences of the traffic lights on Data Delivery in VANETs. We develop an analysis model for evaluating the Data Delivery among the vehicles that move along a path with multiple traffic lights. Based on the model, vehicles can estimate the reachability of destinations and the Data Delivery delay. Thus, we propose a transmission control scheme by the given deadline of reachable destinations, in order to improve the Data Delivery. Our intensive simulations verify the proposed model, and evaluate the influence of the traffic lights on Data Delivery.
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MiSeNet@MobiCom - Catching up with traffic lights for Data Delivery in vehicular ad hoc networks
Proceedings of the 2nd ACM annual international workshop on Mission-oriented wireless sensor networking - MiSeNet '13, 2013Co-Authors: Chao Song, Wei-shih Yang, Ming LiuAbstract:The Data Delivery in vehicular ad hoc networks (VANETs) is based on the wireless communication among vehicles (V2V) and infrastructures (V2I). This Delivery obviously depends on the mobility of the vehicles (e.g. with carry-and-forward). However, the mobility of the vehicles is not only affected by the vehicle itself, but also by some external means, such as the signal operations of traffic lights. The red light stops the vehicles at the intersection, which will increase the Delivery delay of the messages carried by the vehicle with waiting time. However, the red light can also increase the opportunities of vehicles moving behind to catch up with the waited vehicles in forwarding messages. In this paper, we investigate the influence of the traffic lights on Data Delivery in VANETs, and we estimate the expected Data Delivery delay along a path with multiple traffic lights. Our intensive simulations verify the proposed model, and evaluate the influence of the traffic lights on Data Delivery.
Yanmin Zhu - One of the best experts on this subject based on the ideXlab platform.
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trajectory improves Data Delivery in urban vehicular networks
IEEE Transactions on Parallel and Distributed Systems, 2014Co-Authors: Yanmin ZhuAbstract:Efficient Data Delivery is of great importance, but highly challenging for vehicular networks because of frequent network disruption, fast topological change and mobility uncertainty. The vehicular trajectory knowledge plays a key role in Data Delivery. Existing algorithms have largely made predictions on the trajectory with coarse-grained patterns such as spatial distribution or/and the inter-meeting time distribution, which has led to poor Data Delivery performance. In this paper, we mine the extensive Data sets of vehicular traces from two large cities in China, i.e., Shanghai and Shenzhen, through conditional entropy analysis, we find that there exists strong spatiotemporal regularity with vehicle mobility. By extracting mobility patterns from historical vehicular traces, we develop accurate trajectory predictions by using multiple order Markov chains. Based on an analytical model, we theoretically derive packet Delivery probability with predicted trajectories. We then propose routing algorithms taking full advantage of predicted probabilistic vehicular trajectories. Finally, we carry out extensive simulations based on three large Data sets of real GPS vehicular traces, i.e., Shanghai taxi Data set, Shanghai bus Data set and Shenzhen taxi Data set. The conclusive results demonstrate that our proposed routing algorithms can achieve significantly higher Delivery ratio at lower cost when compared with existing algorithms.
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trajectory improves Data Delivery in vehicular networks
International Conference on Computer Communications, 2011Co-Authors: Yanmin ZhuAbstract:Efficient Data Delivery is a great challenge in vehicular networks because of frequent network disruption, fast topological change and mobility uncertainty. The vehicular trajectory knowledge plays a key role in Data Delivery. Existing algorithms have largely made predictions on the trajectory with coarse-grained patterns such as spatial distribution or/and the inter-meeting time distribution, which has led to poor Data Delivery performance. In this paper, we mine the extensive trace Datasets of vehicles in an urban environment through conditional entropy analysis, we find that there exists strong spatiotemporal regularity. By extracting mobile patterns from historical traces, we develop accurate trajectory predictions by using multiple order Markov chains. Based on an analytical model, we theoretically derive packet Delivery probability with predicted trajectories. We then propose routing algorithms taking full advantage of predicted vehicle trajectories. Finally, we carry out extensive simulations based on real traces of vehicles. The results demonstrate that our proposed routing algorithms can achieve significantly higher Delivery ratio at lower cost when compared with existing algorithms.
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INFOCOM - Trajectory improves Data Delivery in vehicular networks
2011 Proceedings IEEE INFOCOM, 2011Co-Authors: Yanmin ZhuAbstract:Efficient Data Delivery is a great challenge in vehicular networks because of frequent network disruption, fast topological change and mobility uncertainty. The vehicular trajectory knowledge plays a key role in Data Delivery. Existing algorithms have largely made predictions on the trajectory with coarse-grained patterns such as spatial distribution or/and the inter-meeting time distribution, which has led to poor Data Delivery performance. In this paper, we mine the extensive trace Datasets of vehicles in an urban environment through conditional entropy analysis, we find that there exists strong spatiotemporal regularity. By extracting mobile patterns from historical traces, we develop accurate trajectory predictions by using multiple order Markov chains. Based on an analytical model, we theoretically derive packet Delivery probability with predicted trajectories. We then propose routing algorithms taking full advantage of predicted vehicle trajectories. Finally, we carry out extensive simulations based on real traces of vehicles. The results demonstrate that our proposed routing algorithms can achieve significantly higher Delivery ratio at lower cost when compared with existing algorithms.
Chao Song - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Red or green: Analyzing the Data Delivery with traffic lights in vehicular ad hoc networks
2014 IEEE Global Communications Conference, 2014Co-Authors: Chao Song, Wei-shih Yang, Ming LiuAbstract:The Data Delivery in Vehicular Ad Hoc Networks (VANETs) depends on the mobility of the vehicles (e.g. with carry-and-forward). However, the mobility of the vehicles is not only affected by the nodes themselves, but also by some external means such as the traffic lights. The red light stops the vehicles at the intersection, which will increase the Delivery delay of the messages carried by the vehicle with waiting time. On the contrary, this may also increase the opportunities of vehicles moving behind to catch up in forwarding messages. In this paper, we investigate the negative and positive influences of the traffic lights on Data Delivery in VANETs. We develop an analysis model for evaluating the Data Delivery among the vehicles that move along a path with multiple traffic lights. Based on the model, vehicles can estimate the reachability of destinations and the Data Delivery delay. Thus, we propose a transmission control scheme by the given deadline of reachable destinations, in order to improve the Data Delivery. Our intensive simulations verify the proposed model, and evaluate the influence of the traffic lights on Data Delivery.
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MiSeNet@MobiCom - Catching up with traffic lights for Data Delivery in vehicular ad hoc networks
Proceedings of the 2nd ACM annual international workshop on Mission-oriented wireless sensor networking - MiSeNet '13, 2013Co-Authors: Chao Song, Wei-shih Yang, Ming LiuAbstract:The Data Delivery in vehicular ad hoc networks (VANETs) is based on the wireless communication among vehicles (V2V) and infrastructures (V2I). This Delivery obviously depends on the mobility of the vehicles (e.g. with carry-and-forward). However, the mobility of the vehicles is not only affected by the vehicle itself, but also by some external means, such as the signal operations of traffic lights. The red light stops the vehicles at the intersection, which will increase the Delivery delay of the messages carried by the vehicle with waiting time. However, the red light can also increase the opportunities of vehicles moving behind to catch up with the waited vehicles in forwarding messages. In this paper, we investigate the influence of the traffic lights on Data Delivery in VANETs, and we estimate the expected Data Delivery delay along a path with multiple traffic lights. Our intensive simulations verify the proposed model, and evaluate the influence of the traffic lights on Data Delivery.
Yozo Shoji - One of the best experts on this subject based on the ideXlab platform.
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Vehicle-Assisted Data Delivery in Smart City: A Deep Learning Approach
IEEE Transactions on Vehicular Technology, 2020Co-Authors: Wei Liu, Yoshito Watanabe, Yozo ShojiAbstract:Collecting the massive internet of things Data produced in a large smart city is quite challenging, and recent advances in vehicle-to-everything communication makes urban vehicles to be a good candidate to conduct this task. Hence, this paper proposes a novel deep learning algorithm called DeepVDD to facilitate vehicle-assisted Data Delivery. First, a theoretical analysis is presented to quantitatively reveal the correlation between vehicle mobility and the success ratio of vehicle-assisted Data Delivery. Based on the findings in analysis, DeepVDD adopts a novel multi-headed neural network to determine the strategies for vehicles to deliver Data. Comprehensive evaluations have been executed based on the real taxi mobility Data in Tokyo, Japan. The results have validated that, compared with other state-of-art algorithms, DeepVDD not only improves the success ratio of Data Delivery, but also significantly reduces the communication overhead of vehicular networks.
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WiMob - Mobility and Terrain-aware Data Delivery in Urban Vehicular Networks
2019 International Conference on Wireless and Mobile Computing Networking and Communications (WiMob), 2019Co-Authors: Wei Liu, Yoshito Watanabe, Yozo ShojiAbstract:Collecting and disseminating the sensing Data in a large smart city is quite challenging, and vehicles moving in the city would be a good candidate to accomplish this task effectively. Hence, this paper proposes a novel Data Delivery algorithm in urban vehicular networks that determines Data forwarding and buffer management strategies according to the mobility pattern of vehicles and city terrain. Extensive evaluations have been executed based on a real taxi mobility Data set that is obtained from a smart city testbed deployed in Tokyo, Japan. The results have validated that, compared with the state-of-art algorithms, our proposal can achieve better success ratio of Data Delivery with less Data transmission overhead in vehicular networks.
Kun Yang - One of the best experts on this subject based on the ideXlab platform.
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ICCC - A scalable gather point based Data Delivery scheme in mobile social networks
2016 IEEE CIC International Conference on Communications in China (ICCC), 2016Co-Authors: Xiang Wang, Supeng Leng, Jiechen Yin, Quanxin Zhao, Kun YangAbstract:Mobile social networks (MSNs) are a special kind of delay tolerant networks that can apply social knowledge of user clustering to improve the performance of Data Delivery scheme in MSNs. However, the existing user clustering schemes ignore the fact that prediction accuracy will decrease with the increase of the network scale, which restricts the scalability of existing Data Delivery schemes and results in the degradation of the network Data Delivery ratio and delay. This paper proposes a two-layer Data Delivery model to increase the scalability for large-scale MSNs. Besides, a Two-Layer QoS-aware Data Delivery (TLD) scheme is proposed with much lower computational complexity than that of the existing algorithms. The obtained simulation results indicate that the proposed TLD scheme outperforms the existing mobility-based MSN schemes in terms of both Delivery ratio and delay.
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GLOBECOM - ESD: An Energy Saving Data Delivery Scheme in Mobile Social Networks
2015 IEEE Global Communications Conference (GLOBECOM), 2015Co-Authors: Xiang Wang, Supeng Leng, Jiechen Yin, Bo Fan, Kun YangAbstract:Mobile social network (MSN) is a special kind of delay tolerant network that consists of mobile users with social characteristics. The existing social-aware Data Delivery algorithms usually ignore the energy cost of devices as well as the time-varying characteristic of user clustering in the vicinity of hotspots, which result in the degradation of the energy efficiency and the delay performance of Data Delivery in the MSN. This paper proposes an Energy Saving Data Delivery (ESD) scheme, which can reduce energy consumption and Data Delivery delay. Moreover, the optimal number of Data copies, the optimal set of destination hotspots and the route paths with the minimum energy cost are derived towards the highest energy efficiency for Data Delivery in a MSN. Simulation results indicate that the proposed ESD scheme outperforms the existing hotspotbased MSN schemes in terms of both energy cost and delay of Data Delivery. We also investigate the impact of the community similarity of users on the performance of Data Delivery.
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ESD: An Energy Saving Data Delivery Scheme in Mobile Social Networks
2015 IEEE Global Communications Conference (GLOBECOM), 2014Co-Authors: Xiang Wang, Supeng Leng, Jiechen Yin, Bo Fan, Kun YangAbstract:© 2015 IEEE. Mobile social network (MSN) is a special kind of delay tolerant network that consists of mobile users with social characteristics. The existing social-aware Data Delivery algorithms usually ignore the energy cost of devices as well as the time-varying characteristic of user clustering in the vicinity of hotspots, which result in the degradation of the energy efficiency and the delay performance of Data Delivery in the MSN. This paper proposes an Energy Saving Data Delivery (ESD) scheme, which can reduce energy consumption and Data Delivery delay. Moreover, the optimal number of Data copies, the optimal set of destination hotspots and the route paths with the minimum energy cost are derived towards the highest energy efficiency for Data Delivery in a MSN. Simulation results indicate that the proposed ESD scheme outperforms the existing hotspotbased MSN schemes in terms of both energy cost and delay of Data Delivery. We also investigate the impact of the community similarity of users on the performance of Data Delivery
