Sensor Density for Confident Information Coverage in Randomly Deployed Sensor Networks

Coverage is one of the fundamental issues in wireless sensor networks, yet most of the current studies on coverage are based on the simplest disk coverage model. Based on the theory of field reconstruction, we proposed a novel coverage model called confident information coverage in our previous study. In this paper, based on the confident information coverage model, we study the critical sensor density to achieve complete coverage in randomly deployed sensor networks. We first use the average vacancy to measure the degree of coverage, and compute the average vacancy through the computation of the probability that an arbitrary point is not covered by randomly deployed sensors within its correlation range. We then propose a numerical computation method called discrete approximation algorithm to compute this probability, and prove that this probability is actually the limit of the output of the proposed algorithm. Furthermore, we derive the upper and lower bound for the average vacancy as a function of sensor density, which provides a useful insight for the critical sensor density to achieve complete coverage. The simulation results validate our theoretical analysis.