A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks

• Published in: Neural computing & applications Vol. 32; no. 12; pp. 7709 - 7723
• Main Authors: Pitchaimanickam, B., Murugaboopathi, G.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Clustering; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Data transmission; Energy consumption; Energy efficiency; Energy transmission; Energy utilization; Heuristic methods; Image Processing and Computer Vision; Nodes; Particle swarm optimization; Probability and Statistics in Computer Science; Remote sensors; Residual energy; Sensors; Soft Computing Techniques: Applications and Challenges; Wireless networks; Wireless sensor networks; Energy consumption; LEACH-C Algorithm; Network lifetime; Wireless Sensor Networks; Firefly Algorithm (FA); Particle Swarm Optimization (PSO)
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-019-04441-0

Wireless Sensor Networks (WSN) are operated on battery source, and the sensor nodes are used for collecting the information from the environment and transmitting the same to the base station. The sensor nodes consume more energy for the process of data communication and also affect the network lifetime. Energy efficiency is one of the important features for designing the sensor networks. Clustering technique is mainly used to perform the energy-efficient data transmission that consumes the minimum energy and also prolongs the lifetime of the network. In this paper, a Hybrid approach of Firefly Algorithm with Particle Swarm Optimization (HFAPSO) is proposed for finding the optimal cluster head selection in the LEACH-C algorithm. The hybrid algorithm improves the global search behavior of fireflies by using PSO and achieves optimal positioning of the cluster heads. The performance of the proposed methodology is evaluated by using the number of alive nodes, residual energy and throughput. The results show the improvement in network lifetime, thus increasing the alive nodes and reducing the energy utilization. While making a comparison with the firefly algorithm, it has been found that the proposed methodology has achieved better throughput and residual energy.