Research Area:  Vehicular Ad Hoc Networks
It is very difficult to establish and maintain end-to-end connections in a vehicle ad hoc network (VANET) as a result of high vehicle speed, long inter-vehicle distance, and varying vehicle density. Instead, a store-and-forward strategy has been considered for vehicle communications. The success of this strategy, however, depends heavily on the cooperation among nodes. Different from exiting store-and-forward solutions, we propose predictive routing based on the hidden Markov model (PRHMM) for VANETS, which exploits the regularity of vehicle moving behaviors to increase the transmission performance. As vehicle movements often exhibit a high degree of repetition, including regular visits to certain places and regular contacts during daily activities, we can predict a vehicles future locations based on the knowledge of past traces and the hidden Markov model. Consequently, the short-term route of a vehicle and its packet delivery probability for a specific mobile destination can be predicted. Moreover, PRHMM enables seamless handoff between vehicle-to-vehicle and vehicle-to-infrastructure communications so that the transmission performance will not be constrained by the vehicle density and moving speed. Simulation evaluation demonstrates that PRHMM performs much better in terms of delivery ratio, end-to-end delay, traffic overhead, and buffer occupancy.
Author(s) Name:  Lin Yao; Jie Wang; Xin Wang; Ailun Chen and Yuqi Wang
Journal name:  IEEE Transactions on Intelligent Transportation Systems
Publisher name:  IEEE
Volume Information:  Volume: 19, Issue: 3, March 2018,Page(s): 889 - 899
Paper Link:   https://ieeexplore.ieee.org/document/7956227