Data aggregation of moving object with hybrid clustering in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes power consumption is the main challenge. Emerging in-network aggregation techniques are increasingly being sought to overcome this constraint and to save precious energy. WSN applications require spatially dense deployment of sensor nodes to achieve full coverage. As a result, sensors observations have spatial correlation. Rate Distortion theory with the help of cluster based communication model can take advantage of this type of correlation. Gathering of moving object data is one of WSNs applications. The aim is to use cluster based communication model for aggregation of moving object data using RD theory. Static cluster based approach has no reclustering overhead but provide low data accuracy. Dynamic cluster based approach has high reclustering overhead but provide high data accuracy. In this paper we propose a hybrid method for clustering that can take advantage of both static clustering and dynamic clustering. Simulation results show the hybrid method caused less energy consumption in comparison with static and dynamic method.