Presenting the Hybrid Algorithm of Honeybee - Harmony in Clustering and Routing of Wireless Sensor Networks

Abstract

Background: Wireless sensor networks are considered as one of the 21st century's most important technologies. Sensors in wireless sensor networks usually have limited and sometimes non-rechargeable batteries, which they are supposed to be preserved for months or even years. That's why the energy consumption in these networks is of a great importance. Objective: One way to improve energy consumption in a wireless sensor network is to use clustering. In clustered networks, one node is known as the cluster head and other nodes as normal members, which normal nodes send the collected data to the cluster head, and the cluster head sends the information to the base station either by a single step or by multiple steps. Method: Using clustering simplifies resource management and increases scalability, reliability, and the network lifetime. Although the cluster formation involves a time- overhead and how to choose the cluster head is another problem, but its advantages are more than its disadvantages. : The primary aim of this study is to offer a solution to reduce energy consumption in the sensor network. In this study, during the selection of cluster heads, Honeybee Algorithm is used and also for routing, Harmonic Search Algorithm is used. In this paper, the simulation is performed by using MATLAB software and the proposed method is compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) and the multi-objective fuzzy clustering algorithm (MOFCA). Result and Conclusion: By simulations of this study, we conclude that this research has remarkably increased the network lifetime with respect to EECS, LEACH, and MOFCA algorithms. In view of the energy constraints of the wireless sensor network and the non-rechargeable batteries in most cases, providing such solutions and using metaheuristic algorithms can result in a significant reduction in energy consumption and, consequently, increase in the network lifetime.