Data fusion in wireless sensor networks

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Wireless 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 spatio-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 in a neighbourhood. This enables the incorporation of the spatio-temporal correlation of neighbouring sensors, to predict future measurements. The SVR predicted value is used by a KF to estimate the actual drift and correct the measurement. Unfortunately, the KF introduces some system errors when used with nonlinear systems. The use of Unscented Kalman filter (UKF) instead, considerably reduces the system error and results in a better drift correction. The use of IMM with the SVRUKF framework allows for reducing the sampling rate which eventually reduces the communication overhead among the sensors and saves the communication energy. In this thesis, we present several solutions for the random and systematic (drift and bias) errors in sensors measurements, for different sensor deployment scenarios. We also consider two drift scenarios, namely smooth and unsmooth drifts. We evaluate the presented algorithms on simulated and real data obtained from the Intel Berkeley Research Laboratory sensor deployment. The results show that our algorithms successfully detect and correct systematic errors (drift and bias) developed in sensors and filters out the noise. Thereby, prolonging the effective lifetime of the network.
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