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Energy-Efficient Cluster Head Selection in Manets Using Firefly Algorithm


Affiliations
1 Department of Electronics and Communication Engineering, St. Joseph’s Institute of Technology, India
2 Department of Artificial Intelligence and Machine Learning, Vemana Institute of Technology, India
3 Department of Computer Science and Engineering, Adhiyamaan College of Engineering, India
4 Department of Mathematics and Computer Science, University of Africa, Nigeria
     

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In mobile ad hoc networks (MANETs), the efficient energy-efficient cluster head selection (EECHS) is a significant issue. When an appropriate cluster head (CH) is chosen, MANETs' energy efficiency improves. Clusters are built in MANETs to facilitate communication between nodes. The EECHS problem has been addressed using a variety of clustering strategies, including distributed clustering and cluster-based routing. However, these approaches require high overhead and complexity, leading to suboptimal performance. Therefore, it is necessary to develop an algorithm that can select the best CH to facilitate efficient energy-efficient communication in MANETs. In this regard, the Firefly Algorithm (FA) has been proposed as an effective approach to solving the EECHS problem. FA is a type of swarm intelligence-based meta-heuristics algorithm inspired by the behavior of fireflies. FA is a simple and efficient optimization method that can be used to select the best CH in MANETs. The method has a number of benefits, including enhanced convergence rate and local search functionality. FA is also computationally effective and can adapt to shifting network conditions. This essay offers a thorough evaluation of the literature on the use of FA in MANETs to address the EECHS. Along with comparisons of their performances, a thorough examination of the suggested methodologies and algorithms is offered. Last but not least, various difficulties and potential research paths pertaining to the usage of the FA are explored.

Keywords

Energy, Cluster Head, Selection, MANET, Firefly Algorithm.
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  • Energy-Efficient Cluster Head Selection in Manets Using Firefly Algorithm

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Authors

G. Rohini
Department of Electronics and Communication Engineering, St. Joseph’s Institute of Technology, India
H.C. Kantharaju
Department of Artificial Intelligence and Machine Learning, Vemana Institute of Technology, India
B. Gopinathan
Department of Computer Science and Engineering, Adhiyamaan College of Engineering, India
Sangeetha Krishnan
Department of Mathematics and Computer Science, University of Africa, Nigeria

Abstract


In mobile ad hoc networks (MANETs), the efficient energy-efficient cluster head selection (EECHS) is a significant issue. When an appropriate cluster head (CH) is chosen, MANETs' energy efficiency improves. Clusters are built in MANETs to facilitate communication between nodes. The EECHS problem has been addressed using a variety of clustering strategies, including distributed clustering and cluster-based routing. However, these approaches require high overhead and complexity, leading to suboptimal performance. Therefore, it is necessary to develop an algorithm that can select the best CH to facilitate efficient energy-efficient communication in MANETs. In this regard, the Firefly Algorithm (FA) has been proposed as an effective approach to solving the EECHS problem. FA is a type of swarm intelligence-based meta-heuristics algorithm inspired by the behavior of fireflies. FA is a simple and efficient optimization method that can be used to select the best CH in MANETs. The method has a number of benefits, including enhanced convergence rate and local search functionality. FA is also computationally effective and can adapt to shifting network conditions. This essay offers a thorough evaluation of the literature on the use of FA in MANETs to address the EECHS. Along with comparisons of their performances, a thorough examination of the suggested methodologies and algorithms is offered. Last but not least, various difficulties and potential research paths pertaining to the usage of the FA are explored.

Keywords


Energy, Cluster Head, Selection, MANET, Firefly Algorithm.

References