An Ant Colony Optimizing Algorithm Based on Scheduling Preference for Maximizing Working Time of WSN

Abstract

With the proliferation of wireless sensor networks (WSN), the issues about how to schedule all the sensors in order to maximize the system's working time have been in the spotlight. Inspired by the promising performance of ant colony optimization (ACO) in solving combinational optimization problem, we attempt to apply it in prolonging the life time of WSN. In this paper, we propose an improved version of ACO algorithm to get solutions about selecting exact sensors to accomplish the covering task in a reasonable way to preserve more energy to maintain longer active time. The methodology is based on maximizing the disjoint subsets of sensors, in other words, in every time interval, choosing which sensor to sustain active state must be rational in certain extent. With the aid of pheromone and heuristic information, a better solution can be constructed in which pheromone denotes the previous scheduling experience, while heuristic information reflects the desirable device assignment. Orderly sensor selection is designed to construct an advisable subset for coverage task. The proposed method has been successfully applied in solving limited energy assignment problem no matter in homogenous or heterogeneous WSNs. Simulation experiments have shown it has a good performance in addressing relevant issues.

Comparative Study of Ant Colony Algorithms for Multi-Objective Optimization

“…It allows decision-makers to obtain optimal non-dominated solutions through the interaction of reference points. Xiao-Min Hu et al [29] proposed an improved ant colony algorithm, which can accurately select the sensor in a reasonable way to complete the coverage task. Thereby, this saves more energy to maintain the sensor working for a long time and achieves the purpose of maximizing the working hours of the wireless sensor.…”