Target tracking in noisy wireless sensor network using artificial neural network

The use of wireless sensor networks (WSN) in tracking applications is growing at a fast pace. In these applications, the sensor nodes discover, monitor and track an event or target object. Wireless sensor networks are by nature harsh, uncertain and dynamic, therefore there are many noise sources which malignantly impact on the performance and the efficiency of a wireless sensor network. On the other hand artificial intelligence method provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. In this paper we investigate application of artificial neural networks to tackle the noise interference in target tracking. Beacon signals help to estimate distances and learn Network Area. Computer simulations showed improvement in tracking accuracy in compare of traditional method.