Load Balanced Clustering Based on Imperialist Competitive Algorithm in Wireless Sensor Networks

Energy consumption is one of the most serious issues in designing Wireless Sensor Networks (WSNs) for maximizing its lifetime and stability. Clustering is considered as one of the topology control methods for maintaining the stability of WSNs which can significantly reduce energy consumption in WSNs...

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Bibliographic Details
Published in:Wireless personal communications Vol. 112; no. 1; pp. 371 - 385
Main Authors: Dehestani, Fahimeh, Jabraeil Jamali, Mohammad Ali
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2020
Springer Nature B.V
Subjects:
Clustering
Communications Engineering
Computer Communication Networks
Computer simulation
Control methods
Control stability
Energy consumption
Engineering
Evolutionary algorithms
Load balancing
Network topologies
Networks
Nodes
Optimization
Sensors
Signal,Image and Speech Processing
Wireless networks
Wireless sensor networks
Wireless sensor network
Imperialist competitive algorithm
Load balancing
Clustering algorithm
ISSN:0929-6212, 1572-834X
Online Access:Get full text
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Summary:Energy consumption is one of the most serious issues in designing Wireless Sensor Networks (WSNs) for maximizing its lifetime and stability. Clustering is considered as one of the topology control methods for maintaining the stability of WSNs which can significantly reduce energy consumption in WSNs. However, using different methods for the selection of cluster head is an important challenge in this domain of research. Load balanced clustering is known as an NP-hard problem for a WSN along with unequal load for sensor nodes. The Imperialist Competitive Algorithm (ICA) is regarded as an evolutionary method which can be used for finding a quick and efficient solution to such problems. In this paper, a clustering method with an evolutionary approach is introduced which investigates the issues of load balance and energy consumption of WSNs in the equal and unequal load modes so as to select optimal cluster heads. Simulation of the proposed method, carried out via NS2, indicated that it improves the criteria of energy consumption, the number of active sensor nodes and execution time.
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ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-020-07030-w