Artificial Bee Colony algorithm for Multi-Target Tracking in mobility sensor networks

In Mobile Sensor Networks, it is important to manage the mobility of the nodes and optimized computation in order to improve the performance and lifetime of the network. Existing methods dealt about only Single Target tracking methods for these criteria. In this paper, Multi-Target Tracking system using Artificial Bee Colony (ABC) Optimization algorithm was proposed to meet these requirements. In this proposed method, current positions of the targets are estimated and the next positions are predicted. Estimation and Prediction are done by using Interval Analysis. In order to cover multi-target in an optimal way to minimize the total travelled distances by nodes, a better decisions regarding the positions where the sensor is able to move upon is done by optimization techniques. Then assigning, each mobile node one new location within the computed set using the ABC Optimization techniques. In Artificial Intelligence, Bee Colony is the only one approach which can be applied to multi-nodal Optimization problem. This technique uses an ABC Optimization algorithm which can be better suit for unconstrained problems. Simulation results on the well-known benchmark functions, shows the efficiency and effectiveness of the proposed algorithm.