Collaborative sensing to improve information quality for target tracking in wireless sensor networks

Due to limited network resources for sensing, communication and computation, information quality (IQ) in a wireless sensor network (WSN) depends on the algorithms and protocols for managing such resources. In this paper, for target tracking application in WSNs consisting of active sensors (such as ultrasonic sensors) in which normally a sensor senses the environment actively by emitting energy and measuring the reflected energy, we present a novel collaborative sensing scheme to improve the IQ using joint sensing and adaptive sensor scheduling. With multiple sensors participating in a single sensing operation initiated by an emitting sensor, joint sensing can increase the sensing region of an individual emitting sensor and generate multiple sensor measurements simultaneously. By adaptive sensor scheduling, the emitting sensor for the next time step can be selected adaptively according to the predicted target location and the detection probability of the emitting sensor. Extended Kalman filter (EKF) is employed to estimate the target state (i.e., the target location and velocity) using sensor measurements and to predict the target location. A Monte Carlo method is presented to calculate the detection probability of an emitting sensor. It is demonstrated by simulation experiments that collaborative sensing can significantly improve the IQ, and hence the tracking accuracy, as compared to individual sensing.