Revised beaconing glowworm swarm optimization ant colony optimization algorithm to localize nodes and optimize the energy consumed by nodes in wireless sensor networks

In the wireless sensor networks energy consumption is broadest and widely explored area of research. The solution for energy optimization encompasses various techniques such as efficient routing protocols, data scheduling, clustering, hardware redesigning, supervised and unsupervised network learning algorithm, and so forth. Compared with all the methods that has been so far discussed, swarm intelligence (SI) is considered to be the optimal way to find solution for reducing energy consumption as it is simple and the network formation is understood by the natural mechanism present in nature. SI approaches include ant colony optimization (ACO), particle swarm optimization, glowworm swarm optimization (GSO), and so forth. In this article, the authors provide the solution for the energy conservation problem through efficient GSO methods combined ACO. The revised beaconing glowworm swarm optimization ant colony optimization algorithm will be applied on the sensor network divided into swarms based on glowworms, the ants are introduced in the network that would parse the network by visiting the swarm heads with the principle of ACO behind it. The algorithm is tested on MATLAB 2015a for performance comparison with the HM-ACOPSO method with depicts energy conservation and efficiency in data collection.

Automated summary

This article introduces a method for solving the energy conservation problem in wireless sensor networks through the combination of ant colony optimization and glowworm swarm optimization algorithms. The algorithm achieves energy saving and efficient data collection by analyzing the network and accessing the swarm heads.