Sensing in Mobile Sensor Networks with Noisy Mobility Knowledge

In this paper, we study the performance of sensing in mobile sensor networks with imperfect knowledge of neighborhood mobility. We examine the impact of exchanging incorrect mobility information on the cost of sensing and the required target coverage. The study is performed for two target coverage models: an independent coverage model and a Markovian one. We demonstrate via extensive simulations that a small amount of the mobility information is required to be successfully exchanged between the mobile sensors to provide the required coverage of targets. Finally, we conduct an experiment at the University of Toronto campus to show the impact of the mobility knowledge in sensing. The experiment results match our expectation in terms of the target coverage, sensing cost, and trade-off between the two metrics.