Prediction based Target Tracking in Wireless Sensor Network (WSN)

Abstract

Wireless Sensor Networks (WSN) are used for monitoring purposes like physical monitoring, environmental monitoring, and area monitoring. WSNs cover many application areas such as security, health, environmental monitoring, industrial monitoring, and target tracking. Target tracking is a technique in which the target location is estimated through the Self-known positioned (SN). These applications require large data collection, data forwarding, and extensive monitoring. To cater all these requirements with scarce resources is a challenging job. To address these issues, we study WSN schemes that rely on the incorporation of sensing data and target tracking to achieve overall accuracy and energy efficiency. There are various solutions in the literature like Global Positioning System (GPS), Radio Frequency Identification (RFID), Received Signal Strength Indi cator (RSSI), Trilateration, and Multilateration but every solution has some drawbacks associated with it. Therefore, a new dynamic clustering based tracking technique is proposed in this thesis to address these issues. The proposed scheme reduces the energy consumption by activating few SN, results in prolonging the WSN lifetime. The results of the adaptive scheme are simulated that depicts an accurate, precision-based target tracking can be achieved using only a few sensors.