High-density wireless sensor networks: a new clustering approach for prediction-based monitoring

Abstract

We propose a new cluster-based approach that simplifies prediction-based monitoring for homogeneous, high-density wireless sensor networks composed of a large number of small, power-restricted nodes. Prediction-based monitoring can increase the autonomous lifetime of the network by reducing communication. In our clustering approach, the cluster-heads spatio-temporally correlate and predict the measurements of the cluster-members by executing their prediction model. Routing is only done by the gateway nodes at the circumference of the clusters while the nongateway nodes, which are positioned between the cluster-heads and their gateway nodes, are allowed to turn off their radio communication as long as their measurements satisfy the predictions of their cluster-head. Turning off radio communication results in high energy savings and can greatly improve system lifetime. Our main contribution is the description of this clustering approach while the prediction models are beyond the scope of this paper.