Adaptive sensor scheduling for target tracking in underwater wireless sensor networks

Abstract

Target tracking is one of the broad applications of underwater wireless sensor networks (UWSNs). Sensors of UWSNs are battery-powered and it is impracticable to replace the batteries when exhausted. That means the batteries life affecting the lifetime of whole networks. So, it is worth reducing the energy consumption on the premise of satisfactory tracking accuracy. This paper proposes an adaptive sensor scheduling scheme that implements for accurately and energy-efficiently tracking a maneuvering target detected by UWSNs. This scheme employs multi-sensor to achieve the tracking task. A priori criterion is presented to select the best sensor group and best fusion sensor from candidate sensors. The criterion is generated from the algorithm combining interacting multiple model with extended Kalman filters (IMM-EKF). For reducing the energy consumption, the sampling interval is variable according to whether the tracking accuracy is satisfactory or not at each time step. Simulation demonstrates that selecting best sensor group can improve the tracking accuracy significantly, and selecting best fusion sensor and appropriate sampling interval can reduce the energy consumption significantly.