Accuracy Based Adaptive Sampling and Multi-Sensor Scheduling for Collaborative Target Tracking

Tracking is an essential capability in many wireless sensor network (WSN) applications. Due to the probabilistic nature of the target movement and the limited detection region and miss detection of sensor, this existing single sensor sensing scheme may result in tracking failure when a scheduled sensor fails to detect the target. We present an adaptive multi-sensor scheduling algorithm for collaborative target tracking in WSNs to improve the tracking reliability and power efficient. This proposed scheme determines the sampling intervals based on the predicted tracking accuracy, and selects a number of sensors to form a temporary tasking group for the next time step, based on a specified detection probability. One of the tasking group members is selected to be the leader node, which acts as a local temporary fusion center