Trade-off between accuracy, cost, and QoS using a beacon-on-demand strategy and Kalman filtering over a VANET

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 considerably 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 proposed 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.