Evaluation ARIMA Modeling-Based Target Tracking Scheme in Wireless Sensor Networks Using Statistical Tests

Wireless sensor networks are composed of large number of sensor nodes which cooperate for monitoring and gathering information about a given environment. These networks have many applications in monitoring and tracking fields. Target tracking is one of the most important applications of wireless sensor networks. Basically, target tracking protocols focus on Energy efficiency, maintenance of tracking accuracy and reducing the number of nodes involved in the tracking process with help of prediction mechanisms. Using accurate prediction mechanisms with low computational complexity has an important role in reducing energy consumption and maintaining tracking accuracy. In this paper, we propose a protocol called Auto-Regressive integrated Moving Average-based Distributed Predictive Tracking (ARIMA-DPT) which presents an accurate model for prediction of target next location using ARIMA time series. To evaluate the significance of presented prediction model, we use statistical tests. To show the accuracy of proposed prediction model we use NS2 simulator.