A false deletion data tracking method based on Fisher information distance in wireless sensor networks

Abstract

To solve the shortcomings of traditional data tracking methods, such as low tracking efficiency, high error rates, high consumption, and so on, we propose a method based on Fisher information distance to track the deleted data in wireless sensor networks. The proposed method adopts a multilateral measurement technology to measure the geometric data and mutual position information among wireless sensors. It also adopts a Kalman filter to reduce noise interference during data processing. It selects the most suitable and pre-selected member component tracking cluster to realise data tracking of wireless sensor networks based on Fisher information distance. Experimental results show that the proposed method has high accuracy, noise elimination ability, and network energy consumption saving capability.