Towards Energy-Efficient Algorithm-Based Estimation in Wireless Sensor Networks

A primary purpose of sensing in a sensor network is to collect and aggregate information about a phenomenon of interest. The batteries on today's wireless sensor barely last a few days, and nodes typically expend a lot of energy in computation and wireless communication. Hence, the energy efficiency of the system is a major issue. Different representative mechanisms has been proposed to achieve a long lived sensors such as “clustering mechanisms” as well as Aggregation techniques to reduce the amount of data communication generated by sensors. Depending on the data type, ARMA series and forecasting are possible ways to reduce data transmission. In this work, we adopt single-hop clustering mechanism where all sensor nodes in a cluster communicate with their Cluster-Head (or sink) via single hop (such as In/On body sensors for personal health monitoring,..). We propose different data aggregation algorithms based on the AutoRegressive model, to predict local readings and reduce the communication traffic. We evaluate the performance of our work in terms of communication cost and energy consumption. We also extend our work to enhance the prediction accuracy by estimating dynamic prediction threshold. Our simulation shows that depending on data type, communication overhead and rate can be reduced and a considerable accuracy prediction can be obtained.