An iterative method for strong barrier coverage under practical constraints
Xiaoyun Zhang, Mathew L. Wymore, Daji Qiao · 2016
Barrier coverage is a fundamental application for wireless sensor networks. In this paper, we consider a practical probabilistic sensing model and propose an iterative scheme, called BaCo, to provide strong barrier coverage under this model, with the objective of minimizing the number of active sensors. Moreover, we build the barrier under practical constraints of minimum detection probability and maximum false alarm probability. We use simulations to show that BaCo converges quickly and achieves better results than previous work while also bounding the system false alarm probability.