On the Coverage Performance of Boolean-Poisson Cluster Models for Wireless Sensor Networks

In this paper, we consider wireless sensor networks (WSNs) with sensor nodes exhibiting clustering in their deployment. We model the coverage region of such WSNs by Boolean Poisson cluster models (BPCM) where sensors nodes' location is according to a Poisson cluster process (PCP) and each sensor has an independent sensing range around it. We consider two variants of PCP, in particular \matern and Thomas cluster process to form Boolean \matern and Thomas cluster models. We first derive the capacity functional of these models. Using the derived expressions, we compute the sensing probability of an event and compare it with sensing probability of a WSN modeled by a Boolean Poisson model where sensors are deployed according to a Poisson point process. We also derive the power required for each cluster to collect data from all of its sensors for the three considered WSNs. We show that a BPCM WSN has less power requirement in comparison to the Boolean Poisson WSN, but it suffers from lower coverage, leading to a trade-off between per-cluster power requirement and the sensing performance. A cluster process with desired clustering may provide better coverage while maintaining low power requirements

Coverage Improvement of Wireless Sensor Networks via Spatial Profile Information

This paper considers a wireless sensor network deployed to sense an environment variable with a known spatial statistical profile. We propose to use the additional information of the spatial profile to improve the sensing range of sensors while allowing some tolerance in their sensing accuracy. We show that the use of this information improves the sensing performance of the total WSN. For this, we first derive analytical expressions for various performance metrics to measure the improvement in the sensing performance of WSN. We then discuss the sensing gains quantitatively using numerical results