A Realistic Sensing Model for Event Area Estimation in Wireless Sensor Networks

A lot of research works have been reported so far for event area localization and estimation in self-organized wireless sensor networks deployed to monitor a region round the clock. In most of the works, it has been assumed that a node is affected whenever it lies within the event region. But in reality, each node does not sense just its point of location but covers a region defined by its sensing range and extracts an aggregated view of the sensed region. Unfortunately, so far no sensing model takes into account this fact. In this paper, a new realistic model of sensing is proposed for continuous event region, and based on that a lightweight localized algorithm is developed to identify a minimal set of boundary nodes based on 0/1 decision predicates to locate and estimate the event area in real time with high precision. Extensive simulation studies and testbed results validate our proposed model and also show that using only elementary integer operations and limited communication, the proposed scheme outperforms existing techniques achieving a 5–10% precision in area estimation with 75–80% reduction in network traffic even for sparse networks.