Abstract
Wireless sensor networks consist of a large number of low power devices equipped with RF links for communication that have numerous military, civil, and environmental monitoring applications. The energy constraints due to limited battery power present several design challenges. We have considered a random sensor network, where the entire network of these sensors acts as a set of distributed datasets. Each of these sensors has its local temporal dataset along with spatial data and the geographical coordinates of a given object or target. In this paper, a cluster based algorithm is proposed for managing and coordinating a sensor network for tracking moving targets by mining global temporal patterns from these datasets and results in the discovery of nonlinear trajectories of moving objects under supervision. The main objective here is to perform in-network aggregation between the data contained in the various datasets to discover global spatio-temporal patterns; the main constraint is that there should be minimal communication among the participating nodes. We present the algorithm and analyze it in terms of the communication costs.