Information-Driven Sensor Selection for Energy-Efficient Human Motion Tracking

In this paper, we address the issue of human motion tracking in a smart space using a wireless sensor network with a small number of ultrasonic sensors. Ultrasonic sensing is preferable in situations where video monitoring is prohibited due to privacy concerns or is ruled out due to its higher cost and energy consumption. Unlike other common tracking techniques, the schemes proposed in this paper do not require the tracked person to wear a tag. In order to conserve energy, a single ultrasonic sensor that provides maximum information gain is selected. We use the Extended Kalman Filter (EKF) which provides robust state estimates from noisy signals as well as an uncertainty measure in the form of the state covariance, and propose the use of a process model which copes better with missed detections compared to the commonly used constant velocity process model. We propose two sensor selection schemes: (i) Current Node Sensor Selection (CNSS), and (ii) Distributed Neighbourhood node Sensor Election (DNSE), and evaluate their performance in terms of tracking accuracy, target detection ratio and sensor network lifetime.