An Energy-efficient Predictive Model for Object Tracking Sensor Network

ABSTRACT - Among different applications of Wireless Sensor Networks (WSNs), Object Tracking Sensor Network (OTSN) is one of the most attractive applications. In WSN, power management plays a vital role in its overall performance. Hence, a reliable predicative method is proposed in this paper to reduce energy consumption of the nodes in OTSN. This strategy makes use of Machine Learning (ML) technique in order to predict future behaviours of a Mobile Object (MO) moving into a coverage range of WSN. Moreover, a simulator capable of reproducing the main features of a real MO in an OTSN is developed. Finally, several experiments have been carried out to evaluate the performance and reliability of the proposed methodology in terms of predictions accuracy and energy conservation.

Date: September 2018 -> December 2018

Technologies Used: Python3 | Scikit-Learn | Linux OS

GitHub: OTSN