Distributed Recursive Algorithm for Auto Calibration in Drift Aware Wireless Sensor Networks

The purpose for wireless sensor networks is to deploy low cost sensors with sufficient computing and communication capabilities to support networked sensing applications. The emphasis on lower cost led to sensors that are less accurate and less reliable than their wired sensor counterparts. Sensors usually suffer from both random and systematic bias problems. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to biased sensor measurements. The drift in this context is defined as a unidirectional long-term change in the sensor measurement. We assume that neighboring sensors have correlated measurements and that the instantiation of drift in a sensor is uncorrelated with other sensors. As an extension of our results in [1], and inspired by the resemblance of registration problem in radar target tracking, we propose a distributed recursive Bayesian algorithm for auto calibration of wireless sensors in the presence of slowly varying drifts. The algorithm detects and corrects sensor drifts and improves the reliability and the effective life of the network.