Due to restricted network resources for sensing, communication and computation, info 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 (like ultrasonic sensors) in which normally a sensor senses the environment actively by emitting energy and measuring the mirrored energy, we have a tendency to present a unique collaborative sensing theme to improve the IQ using joint sensing and adaptive sensor scheduling. With multiple sensors taking part in an exceedingly single sensing operation initiated by an emitting sensor, joint sensing can increase the sensing region of a personal emitting sensor and generate multiple sensor measurements simultaneously. By adaptive sensor scheduling, the emitting sensor for the following time step will be selected adaptively consistent with the predicted target location and the detection likelihood of the emitting sensor. Extended Kalman filter (EKF) is used to estimate the target state (i.e., the target location and velocity) using sensor measurements and to predict the target location. A Monte Carlo technique is presented to calculate the detection probability of an emitting sensor. It is demonstrated by simulation experiments that collaborative sensing will considerably improve the IQ, and hence the tracking accuracy, as compared to individual sensing.
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