Data window aggregation techniques for energy saving in Wireless Sensor Networks

Abstract

Redundant data transmissions are likely to happen repeatedly inWireless Sensor Networks (WSNs). According to the literature survey, energy-efficiency predominately relies on data aggregation rather than routing or duty-cycling approaches. As data redundancy dominates the power usage in communication costs, in order to identify and reduce the redundant data transmissions by individual nodes, we propose a hybrid data aggregative window function (DAWF) algorithm for exploiting both spatial and temporal data redundancies in WSNs. Furthermore, the proposed novel approach aims to process the hybrid filtration using both compressive and prediction-based techniques in sensor nodes (SN) as well as in cluster-head (CH) nodes. In this regard, the experimental study case of this work show that the DAWF mechanism can suppress a huge amount of temporal redundant data transmissions in sensor nodes while providing reliable data messages towards the base station (BS). Moreover, DAWF CH can also suppress a large amount of spatial redundancies by utilizing the optimum DAWF parameters of the CH node.