A Matrix Completion Approach to Reduce Energy Consumption in Wireless Sensor Networks

Abstract

The main challenge faced by wireless sensor networks today is the problem of power consumption at the sensor nodes. Over time, researchers have developed different strategies to address this issue. Such strategies are strongly model dependent and/or application specific. In this work, we take a fresh look at the problem of power consumption in wireless sensor networks from a signal processing perspective. The main idea is simple. Sample only a subset of all the sensor nodes at a given instant and transmit them (this reduces both sampling and communication cost for all the nodes combined). At the central unit (sink) use smart mathematical tools (matrix completion algorithms) to estimate the data for the entire network. We have showed that, if about 1% reconstruction error is allowed, only 20% of the sensors need to sample and transmit at a given instant. This means on an average the life of the network is increased 5-fold. If more error reconstruction error is allowed, even lesser number of sensors need to be active at a given instant leading to more prolonged life of the network.