Data fusion techniques for auto calibration in wireless sensor networks

Wireless sensor networks are deployed for the purpose of sensing and monitoring an area of interest. Sensor measurements in sensor networks usually suffer from both random errors (noise) and systematic errors (drift and bias). Even when the sensors are properly calibrated at the time of deployment, they develop errors in their readings leading to erroneous inferences to be made by the network. In this paper we present a novel algorithm for detecting and correcting sensor measurement errors by utilising the spatio-temporal correlation among the neighbouring sensors. The algorithm is designed for sparsely deployed wireless sensor networks. It can follow and correct both slowly and suddenly changing sensor measurements. As a result, the algorithm can adapt for under sampling the sensor measurements. Therefore, it allows for reducing the communication between sensors to maintain the calibration which leads to reducing the energy consumed from the batteries. The algorithm runs recursively and is totally decentralized. We demonstrate using real data obtained from the Intel Berkeley Laboratory that our algorithm successfully suppresses errors developed in sensors and thereby prolongs the effective lifetime of the network.