Spatial correlation aware protocols for efficient data aggregation of moving object in wireless sensor networks

Abstract

In Wireless Sensor Networks (WSNs), power consumption of sensor nodes is the main constraint. Emerging in-network aggregation techniques are increasingly being sought after to address this key challenge and to save precious energy. One application of WSNs is in data gathering of moving objects. In order to achieve complete coverage, this type of application requires spatially dense sensor deployment, which, under close observation, exhibits important spatial correlation characteristics. The Rate Distortion (RD) theory is a data aggregation technique that can take advantage of this type of correlation with the help of a cluster based communication model. Due to object movement, the Rate-Distortion based aggregation incurs high computation overhead. This paper first introduces an introduction for the rate-distortion based moving object data aggregation model. Then, to overcome the high computation overhead, several low overhead protocols are proposed based on this model, namely, a static cluster-based protocol that uses static clustering, a dynamic cluster-based protocol that uses dynamic clustering, and a hybrid protocol which takes advantage of the other two protocols. Simulation results show that with the hybrid method, it is possible to save more than 36% of the nodes’ energy when compared to the other approaches