The Experts below are selected from a list of 41742 Experts worldwide ranked by ideXlab platform
Pietro Manzoni - One of the best experts on this subject based on the ideXlab platform.
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an infrastructureless approach to estimate vehicular Density in urban environments
Sensors, 2013Co-Authors: Julio A Sanguesa, Francisco J Martinez, Juancarlos Cano, Carlos T Calafate, Manuel Fogue, Piedad Garrido, Pietro ManzoniAbstract:In Vehicular Networks, communication success usually depends on the Density of Vehicles, since a higher Density allows having shorter and more reliable wireless links. Thus, knowing the Density of Vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, Vehicle Density is highly variable in time and space. This paper deals with the importance of predicting the Density of Vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between Vehicles. We propose a novel mechanism to estimate the vehicular Density in urban environments. Our mechanism uses as input parameters the number of beacons received per Vehicle, and the topological characteristics of the environment where the Vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular Density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes.
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realistic radio propagation models rpms for vanet simulations
Wireless Communications and Networking Conference, 2009Co-Authors: Francisco J Martinez, Juancarlos Cano, Carlos T Calafate, Pietro ManzoniAbstract:Deploying and testing Vehicular Ad hoc Networks (VANETs) involves high cost and intensive labor. Hence simulation is a useful alternative prior to actual implementation. Most works found in the literature employ very simplistic Radio Propagation Models (RPMs), ignoring the dramatic effects presented by buildings on radio signals. In this paper, we present three different RPMs that increase the level of realism, thereby allowing us to obtain more accurate and meaningful results. These models are: (a) the Distance Attenuation Model (DAM), (b) the Building Model (BM), and (c) the Building and Distance Attenuation Model (BDAM). We evaluated these different models and compared them with the Two-ray Ground model implemented in ns-2. We then carried out further study to evaluate the impact of varying some important parameters such as Vehicle Density and building size on VANET warning message dissemination. Simulation results confirmed that our proposed BDAM significantly affects the percentage of blind Vehicles present and the number of received warning messages, and that our models can better reflect realistic scenarios.
Jiannong Cao - One of the best experts on this subject based on the ideXlab platform.
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adaptive traffic light control of multiple intersections in wsn based its
Vehicular Technology Conference, 2011Co-Authors: Binbin Zhou, Jiannong CaoAbstract:We investigate the problem of adaptive traffic light control of multiple intersections using real-time traffic data collected by a wireless sensor network (WSN). Previous studies mainly focused on optimizing the intervals of green lights in fixed sequences of traffic lights and ignored the traffic flow's characteristics and special traffic circumstances. In this paper, we propose an adaptive traffic light control scheme that adjusts the sequences of green lights in multiple intersections based on the real- time traffic data, including traffic volume, waiting time, number of stops, and Vehicle Density. Subsequently, the optimal green light length can be calculated from the local traffic data and traffic condition of neighbor intersections. Simulation results demonstrate that our scheme produces much higher throughput, lower average waiting time and fewer number of stops, compared with three control approaches: the optimal fixed-time control, an actuated control and an adaptive control.
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adaptive traffic light control in wireless sensor network based intelligent transportation system
Vehicular Technology Conference, 2010Co-Authors: Binbin Zhou, Jiannong Cao, Xiaoqin ZengAbstract:We investigate the problem of adaptive traffic light control using real-time traffic information collected by a wireless sensor network (WSN). Existing studies mainly focused on determining the green light length in a fixed sequence of traffic lights. In this paper, we propose an adaptive traffic light control algorithm that adjusts both the sequence and length of traffic lights in accordance with the real time traffic detected. Our algorithm considers a number of traffic factors such as traffic volume, waiting time, Vehicle Density, etc., to determine green light sequence and the optimal green light length. Simulation results demonstrate that our algorithm produces much higher throughput and lower Vehicle's average waiting time, compared with a fixed-time control algorithm and an actuated control algorithm. We also implement proposed algorithm on our transportation testbed, iSensNet, and the result shows that our algorithm is effective and practical.
Binbin Zhou - One of the best experts on this subject based on the ideXlab platform.
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adaptive traffic light control of multiple intersections in wsn based its
Vehicular Technology Conference, 2011Co-Authors: Binbin Zhou, Jiannong CaoAbstract:We investigate the problem of adaptive traffic light control of multiple intersections using real-time traffic data collected by a wireless sensor network (WSN). Previous studies mainly focused on optimizing the intervals of green lights in fixed sequences of traffic lights and ignored the traffic flow's characteristics and special traffic circumstances. In this paper, we propose an adaptive traffic light control scheme that adjusts the sequences of green lights in multiple intersections based on the real- time traffic data, including traffic volume, waiting time, number of stops, and Vehicle Density. Subsequently, the optimal green light length can be calculated from the local traffic data and traffic condition of neighbor intersections. Simulation results demonstrate that our scheme produces much higher throughput, lower average waiting time and fewer number of stops, compared with three control approaches: the optimal fixed-time control, an actuated control and an adaptive control.
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adaptive traffic light control in wireless sensor network based intelligent transportation system
Vehicular Technology Conference, 2010Co-Authors: Binbin Zhou, Jiannong Cao, Xiaoqin ZengAbstract:We investigate the problem of adaptive traffic light control using real-time traffic information collected by a wireless sensor network (WSN). Existing studies mainly focused on determining the green light length in a fixed sequence of traffic lights. In this paper, we propose an adaptive traffic light control algorithm that adjusts both the sequence and length of traffic lights in accordance with the real time traffic detected. Our algorithm considers a number of traffic factors such as traffic volume, waiting time, Vehicle Density, etc., to determine green light sequence and the optimal green light length. Simulation results demonstrate that our algorithm produces much higher throughput and lower Vehicle's average waiting time, compared with a fixed-time control algorithm and an actuated control algorithm. We also implement proposed algorithm on our transportation testbed, iSensNet, and the result shows that our algorithm is effective and practical.
Jamil Y. Khan - One of the best experts on this subject based on the ideXlab platform.
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distributed spatial reuse distance control for basic safety messages in sdma based vanets
Vehicular Communications, 2015Co-Authors: Muhammad Awais Javed, Duy T Ngo, Jamil Y. KhanAbstract:The periodic generation and transmission of basic safety messages (BSM) place a heavy burden on the load of a vehicular ad hoc network (VANET), where there also remain multiple packet collisions undetected due to the hidden terminals. In this paper, we propose a distributed scheme that controls the spatial reuse distance to improve the efficiency of BSM transmissions in space-division multiple access (SDMA) based VANETs. With the proposed SDMA structure, only Vehicles located sufficiently far apart are allowed to reuse the same time slot to send their BSMs. The signal interference is thus reduced while the hidden-node problem is essentially alleviated. Multiple transmissions per SDMA road segment or 'cell' are also enabled using the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), giving rise to a much better use of both space and bandwidth. To guarantee that the actual number of BSM transmissions does not exceed the maximum allowable in each SDMA cell, we further devise an adaptive scheme that adjusts the spatial reuse distance in accordance with the Vehicle Density. Because the global information of Vehicle Density is not available at individual Vehicles, we propose a distributed algorithm that estimates the Vehicle Density and makes consensus to enhance the accuracy of spatial reuse distance estimation. As the transmission range is controlled accordingly, the mutual interference among the SDMA cells is further reduced. Importantly, the developed control algorithm can be distributively implemented by each Vehicle with limited information exchange. To optimize the performance of the proposed solution, we also determine the optimal bandwidth utilization that maximizes the newly-defined criterion termed as 'safe range,' an important figure-of-merit in vehicular safety applications. Simulation results confirm the clear advantages of our proposal over the available approaches in terms of safety range, packet reception rate, end-to-end delay and BSM inter-arrival time in realistic network scenarios.
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A Cooperative Safety Zone Approach to Enhance the Performance of VANET Applications
2013 IEEE 77th Vehicular Technology Conference (VTC Spring), 2013Co-Authors: Muhammad A. Javed, Jamil Y. KhanAbstract:Safety applications in VANETs relies on various type of information exchanges among Vehicles of which Cooperative Awareness Message (CAM) has the largest share in the network load due to their periodic broadcast nature. As the Vehicle Density grows, the load generated by the CAMs could increase beyond the transmission capacity of a VANET resulting in the loss of neighborhood information and a higher utilization of the control channel. In this paper, we present the concept of a safety zone to adaptively control the transmit power of CAMs to restrict the network load without compromising the safety features of VANET applications. Moreover, we also introduce a new cooperative information sharing technique to increase the Vehicle's awareness beyond the transmission range. The simulation results show that the proposed technique could significantly reduce the packet losses and channel utilization for a range of Vehicle densities.
Xiaoqin Zeng - One of the best experts on this subject based on the ideXlab platform.
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adaptive traffic light control in wireless sensor network based intelligent transportation system
Vehicular Technology Conference, 2010Co-Authors: Binbin Zhou, Jiannong Cao, Xiaoqin ZengAbstract:We investigate the problem of adaptive traffic light control using real-time traffic information collected by a wireless sensor network (WSN). Existing studies mainly focused on determining the green light length in a fixed sequence of traffic lights. In this paper, we propose an adaptive traffic light control algorithm that adjusts both the sequence and length of traffic lights in accordance with the real time traffic detected. Our algorithm considers a number of traffic factors such as traffic volume, waiting time, Vehicle Density, etc., to determine green light sequence and the optimal green light length. Simulation results demonstrate that our algorithm produces much higher throughput and lower Vehicle's average waiting time, compared with a fixed-time control algorithm and an actuated control algorithm. We also implement proposed algorithm on our transportation testbed, iSensNet, and the result shows that our algorithm is effective and practical.
