Energy-Efficient Relay Tracking and Predicting Movement Patterns with Multiple Mobile Camera Sensors

Camera sensor networks (CSN) have been widely used in different applications such as large building monitoring, social security, and target tracking. With advances in visual and actuator sensor technology in the last few years, deploying mobile cameras in CSN has become a possible and efficient solution for many CSN applications. However, mobile camera sensor networks still face several issues, such as limited sensing range, the optimal deployment of camera sensors, and the energy consumption of the camera sensors. Therefore, mobile cameras should cooperate in order to improve the overall performance in terms of enhancing the tracking quality, reducing the moving distance, and reducing the energy consumed. In this paper, we propose a movement prediction algorithm to trace the moving object based on a cooperative relay tracking mechanism. In the proposed approach, the future path of the target is predicted using a pattern recognition algorithm by applying data mining to the past movement records of the target. The efficiency of the proposed algorithms is validated and compared with another related algorithm. Simulation results have shown that the proposed algorithm guarantees the continuous tracking of the object, and its performance outperforms the other algorithms in terms of reducing the total moving distance of cameras and reducing energy consumption levels. For example, in terms of the total moving distance of the cameras, the proposed approach reduces the distance by 4.6% to 15.2% compared with the other protocols that do not use prediction.