Crowd Sensing in Vehicular Networks Using Uncertain Mobility Information

Abstract

In this paper, we quantify the impact of uncertainty in predicted mobility information on the sensing cost of the mobile sensing problem (i.e. sensing of target areas) in vehicular sensor networks. We focus on vehicular sensor networks in which the communication channel is the bottleneck of the system, and sensing the environment is meant to be provided with a minimum load on the communication channel. First, we formulate an opportunistic scheduler, which does not utilize predicted mobility information in the sensing procedure. Then, we formulate a strategic scheduler, which utilizes predicted mobility information in the sensing procedure. After that, we propose two types of noise models that capture the uncertainty in knowing mobility information. We quantify the impact of uncertainty in knowing mobility information on the sensing cost of the strategic scheduler using an independent coverage model. We find that the strategic scheduler outperforms the noise-free opportunistic scheduler even when the strategic scheduler utilizes noisy mobility information up to a certain threshold of noise. This threshold is determined in the paper, and is referred to as the breaking point of the strategic scheduler. Simulation results of the breaking point closely matches our analysis, which demonstrates the correctness of the used analysis methodology. Finally, we use an estimation model for the mobility information between two points on the road, and we propose an algorithm to keep the scheduling scheme below the breaking point.