CROSS: A probabilistic constrained random sensor selection scheme in wireless sensor networks

Due to the application-specific nature of wireless sensor networks, the sensitivity to coverage and data reporting latency varies depending on the type of applications. In light of this, algorithms and protocols should be application-aware to achieve the optimum use of highly limited resources in sensors and hence to increase the overall network performance. This paper proposes a probabilistic constrained random sensor selection (CROSS) scheme for application-aware sensing coverage with a goal to maximize the network lifetime. The CROSS scheme randomly selects in each round (approximately) k data-reporting sensors which are sufficient for a user/application-specified desired sensing coverage (DSC) maintaining a minimum distance between any pair of the selected k sensors. We exploit the Poisson sampling technique to force the minimum distance. Consequently, the CROSS improves the spatial regularity of randomly selected k sensors and hence the fidelity of satisfying the DSC in each round, and the connectivity among the selected sensors increase. To this end, we also introduce an algorithm to compute the desired minimum distance to be forced between any pair of sensors. Finally, we present the probabilistic analytical model to measure the impact of the Poisson sampling technique on selecting k sensors, along with the optimality of the desired minimum distance computed by the proposed algorithm.