Detecting movements and predicting the future path of a moving object in Wireless Sensor Network

Abstract

A Wireless Sensor Network (WSN) is a deployment of several small, inexpensive, self-powered sensors that can sense, compute, and communicate with other sensors for gathering information to make a global decisions about the physical environment. One of the recent applications in wireless sensor network is to detect and predict the movements of a moving object in WSN. It is a challenging task due to high mobility of the object, processing of data acquired from object, failure of sensor nodes, and communication between the sensors using wireless medium. To address the challenge of detecting and predicting the movement of moving object we propose a FaceTrack framework. In this approach the region is divided into different polygons called Faces. This framework estimates the probability of the target moving towards any face. In this framework, to detect a target in a polygon only one sensor stays active at any instant of time. Once the target is detected and it enters the edge of polygon the nodes of the neighboring polygon is activated. Then, activated polygon keep tracking the target. This approach also focuses on predicting the probable future path of a moving object based on historical data about past movements of other objects using data mining algorithm like K-means and Apriori algorithm.