Tuning in-sensor data filtering to reduce energy consumption in wireless sensor networks

In recent years, research on wireless sensor networks has been undergoing a revolution, promising to have significant impact on a broad range of applications from military to health care to food safety. An important problem in many sensor network applications is to decide the amount of computation (or filtering) that needs to be done in the sensor nodes before the data are shifted to a central base station. Right amount of data filtering in the sensor nodes can lead to large savings in network-wide energy consumption. The main goal of this paper is to develop an automated strategy for data filtering in wireless sensor nodes. Assuming that one needs to reduce the overall energy consumption (as opposed to reducing just computation energy or communication energy), the proposed strategy attempts to strike a balance between computation energy consumption and communication energy consumption. Our experimental results clearly indicate that the proposed data filtering strategy generates substantial energy savings in practice.