Tree-Based Neighbor Discovery in Urban Vehicular Sensor Networks

Abstract

In urban vehicular sensor networks, vehicles equipped with onboard sensors monitor some area, and the result can be shared to neighbor vehicles to correct their own sensing data. However, due to the frequent change of vehicle topology compared to the wireless sensor network, it is required for a vehicle to discover neighboring vehicles. Therefore, efficient neighbor discovery algorithm should be designed for vehicular sensor networks. In this paper, two efficient tree-based neighbor discovery algorithms in vehicular sensor networks are proposed and analyzed. After suggesting detailed scenario and its system model, we show that the expected value of neighbor discovery delay has different characteristics depending on neighbor discovery algorithms. An interesting observation of our result is that M-binary tree-based neighbor discovery shows better performance than M-ary tree-based neighbor discovery in the parking lot scenario, which is a counterintuitive result. We analyze why such result appears extensively.