Vehicular Ad-hoc Networks （VANETs） have been suggested as an active and powerful field of research to mitigate environmental problems and challenges. The main challenge in a VANET is to ensure routing with a good Qu...Vehicular Ad-hoc Networks （VANETs） have been suggested as an active and powerful field of research to mitigate environmental problems and challenges. The main challenge in a VANET is to ensure routing with a good Quality of Service （QoS）. The Greedy Perimeter Stateless Routing （GPSR） protocol is one of the most promising position- based routing mechanisms used to overcome this challenge. Its effectiveness depends entirely on the information on a node＇s mobility and the precision of this information. By broadcasting periodic beaconing within trans- mission boundary ranges, GPSR can manage neighbors＇ mobility information and maintain up-to-date lists of neighbours. Nevertheless, information on the position of a neighboring vehicle quickly becomes outdated, which negatively influences the efficiency of the routing. In order to monitor information mobility and to increase the QoS in this challenging area, position estimation needs to he considered. Thus, in this study, we examine the position estimation problem, and propose an improvement to the GPSR protocol, named KF-GPSR, where each vehicle estimates in real time the position of its neighbors using the Kalman filter algorithm. Indeed, by employing this strong estimation technique, it is possible to reduce consid- erably the frequency of exchanged beacon packets, while maintaining high position accuracy. For greater reliability, we also propose an extension to KF-GPSR, called BOD-KF-GPSR, that uses the ＂beacon-on-demand＂ process only if a node needs to rediscover its neighborhood. Simulation experiments using the network simulator NS-2 are presented to demonstrate the ability and usefulness of our two proposals. Here, we compare the pro- posed protocols against diverse common protocols： GPSR, AODV, DSR, and ZRP. The results show that BOD-KF- GPSR achieves a significant enhancement in terms of its packet delivery ratio, routing cost, normalized routing load, end-to-end delay, and throughput.展开更多
文摘Vehicular Ad-hoc Networks （VANETs） have been suggested as an active and powerful field of research to mitigate environmental problems and challenges. The main challenge in a VANET is to ensure routing with a good Quality of Service （QoS）. The Greedy Perimeter Stateless Routing （GPSR） protocol is one of the most promising position- based routing mechanisms used to overcome this challenge. Its effectiveness depends entirely on the information on a node＇s mobility and the precision of this information. By broadcasting periodic beaconing within trans- mission boundary ranges, GPSR can manage neighbors＇ mobility information and maintain up-to-date lists of neighbours. Nevertheless, information on the position of a neighboring vehicle quickly becomes outdated, which negatively influences the efficiency of the routing. In order to monitor information mobility and to increase the QoS in this challenging area, position estimation needs to he considered. Thus, in this study, we examine the position estimation problem, and propose an improvement to the GPSR protocol, named KF-GPSR, where each vehicle estimates in real time the position of its neighbors using the Kalman filter algorithm. Indeed, by employing this strong estimation technique, it is possible to reduce consid- erably the frequency of exchanged beacon packets, while maintaining high position accuracy. For greater reliability, we also propose an extension to KF-GPSR, called BOD-KF-GPSR, that uses the ＂beacon-on-demand＂ process only if a node needs to rediscover its neighborhood. Simulation experiments using the network simulator NS-2 are presented to demonstrate the ability and usefulness of our two proposals. Here, we compare the pro- posed protocols against diverse common protocols： GPSR, AODV, DSR, and ZRP. The results show that BOD-KF- GPSR achieves a significant enhancement in terms of its packet delivery ratio, routing cost, normalized routing load, end-to-end delay, and throughput.
