PBS: A virtual grid architecture for gradient-based active querying in sensor networks

Every physical event diffuses its effect geographically, which results in perceivable information gradient within the proximity of the phenomenon. In this paper, we propose a novel framework for on-demand query processing that exploits this diffusion property to form a virtual grid-based querying architecture, probe-before-spray (PBS), for wireless sensor networks. PBS effectively divides the sensor field on-demand based on the query type and parameters in addition to the gradient spread. Also, it combines gradient routing and in-network processing for efficient and scalable querying in sensor networks. Based on PBS, we design new algorithms to process basic aggregate queries - count, sum, average, max and min, and combined queries. Through analysis, we analyze the worst-case overhead to process these queries using PBS. Also, using extensive simulations, we demonstrate that PBS helps to reduce search overhead significantly (over 30%) to process such queries while attaining accuracy over 99%.