Data fusion in wireless sensor networks

W IRELESS Sensor Networks (WSNs) are deployed for the purpose of monitoring an area of interest. Even when the sensors are properly calibrated at the time of deployment, they develop drift in their readings leading to erroneous network inferences. Traditionally, such errors are corrected by site visits where the sensors are calibrated against an accurately calibrated sensor. For large scale sensor networks, the process is manually intensive and economically infeasible. This imposes finding automatic procedures for continuous calibration. Noting that a physical phenomenon in a certain area follows some spatia-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that measurement errors due to faulty equipment are likely to be uncorrelated. Based on these assumptions, we follow a Bayesian framework to solve the drift and bias problem. in WSNs. In the case of densely deployed WSN, neighbouring sensors are assumed to be close to each other that they observe the same phenomenon. Hence, the average of their corrected readings is taken as a basis for each sensor to self-assess its measurement, estimate its drift and to correct the measurement using a Kalman Filter (KF) in the case of smooth drift, and the Interacting Multiple Model algorithm (IMM) in the case of unsmooth drift. The solutions are computationally simple, decentralised and also scalable. Any new node joining the neighbourhood needs only to obtain the corrected readings of its neighbours to find the average and apply the KF iterative procedure. On the other hand, when the sensors are not densely deployed, Support Vector Regression (SVR) is used to model the interrelationships of sensor measurements