An Energy-Efficient Partition-Based Framework With Continuous Ant Colony Optimization for Target Tracking in Mobile Sensor Networks

Abstract

Target tracking is one of the most common applications in mobile sensor networks. However, since mobile sensors are often battery powered, determining how to schedule the movements of mobile sensors to reduce energy consumption remains an important and challenging task. In this paper, a partition-based target tracking framework with a modified continuous ant colony optimization approach is proposed to achieve flexible and energy-efficient tracking. In the proposed framework, the sensing area is divided into subregions, and the scopes of movement of the mobile sensors are limited to the corresponding subregions to balance the energy consumption among sensors. A modified continuous ant colony optimization method is proposed to adaptively adjust the parameters of the tracking system (e.g., the sensing radius of mobile sensors) in each time instant, minimize the energy cost of the tracking system and yield a satisfactory tracking accuracy. The simulation results indicate that the proposed framework offers promising performance.