Approximate and Sublinear Spatial Queries for Large-Scale Vehicle Networks

With advances in vehicle-to-vehicle communication, future vehicles will have access to a communication channel through which messages can be sent and received when two get close to each other. This enabling technology makes it possible for authenticated users to send queries to those vehicles of interest, such as those that are located within a geographic region, over multiple hops for various application goals, such as ride sharing, interest query, among others. However, naive methods for spatial queries usually require sending the queries to each active vehicle in a region through flooding methods, which will incur communication overhead proportional to the density of vehicles. In this paper, we study the problem of spatial queries for vehicle networks by investigating probabilistic approaches, where we only try to obtain approximate estimates within desired error bounds using sublinear overheads. We demonstrate that by making this relaxation, we can develop approximate query methods for spatial-temporal application requirements such that their overhead is considerably lower than conventional methods. Our key contributions include a random-walk forest based query dissemination method and a topology-aware estimation aggregation method for better algorithm convergence time and estimation accuracy. We use simulated real-world traces to test the proposed methods, and our experimental results show that our proposed method can indeed provide desired estimation accuracy with a high efficiency.