Sensor selection and scheduling for tracking a moving object in a wireless sensor network using the Markov additive chain model

Target detection or tracking along a line has many applications in military border security, behavioral study of herbivorous animals foraging along a track, monitoring suspicious movements around a rectangular area etc. We can make use of a suitable Wireless Sensor Network to achieve the above objectives. Under such conditions, a new method of selecting and scheduling of sensor nodes for tracking a moving object is presented. The objective is to reduce the energy consumption which in turn increases the life of the wireless sensor network. In this work, the inherently present temporal or spatial correlation of the path of a moving object is used to predict the next location of the object. In the proposed method, a Markov Additive Chain Model is used to predict the location of the moving object. Based on the prediction, a dynamic awakening schedule is adopted to minimize the expected cost of the awakening process. Here, we use the probabilistic model for minimizing the cost of detection or tracking.