A Teaching Learning Based Optimization Algorithm for Cluster Head Selection in Wireless Sensor Networks

Yadav, Amita; Kumar, Satish

doi:10.14257/ijfgcn.2017.10.1.10

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…In the year 2017, Yadav and Kumar proposed a teaching learning-based optimization (TLBO) algorithm based on the LEACH protocol (LEACH-T) for CH selection in WSNs [121]. The TLBO algorithm is based on the classroom concept of teacher and learner.…”

Section: Heterogeneitymentioning

confidence: 99%

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

Raj

Ahmedy

Idris

et al. 2022

Wireless Communications and Mobile Computing

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In recent years, wireless sensor networks (WSNs) have been growing rapidly because of their ability to sense data, communicate wirelessly, and compute data efficiently. These networks contain small and low-powered sensor nodes that organize and configure themselves to carry out their functions. Even though WSNs are cheap, easy to deploy, flexible, and efficient, there are some challenges in terms of energy efficiency and network lifetime. Clustering in WSNs is the most reliable solution for the challenges, in which nodes are grouped into few clusters, and a cluster head (CH) is selected for data aggregation and data transfer to the base station (BS). However, there are still many challenges such as energy hole and isolated node problems that exist because of inefficient CH selection and cluster formation methods. In this work, we comprehensively reviewed various nonmetaheuristic and metaheuristic methods for CH selection and cluster formation that are used in networks from various environmental settings, for a better understanding of how the aforementioned problems are tackled by some authors. Moreover, the methods’ parameter settings, advantages, limitations, and future directions are presented with a brief performance summary of the approaches.

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Section: Heterogeneitymentioning

confidence: 99%

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

Raj

Ahmedy

Idris

et al. 2022

Wireless Communications and Mobile Computing

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“…This dynamic multiple swarm optimization process‐based cluster head selection scheme inferred superior results in terms of total data received from the base station, first node died and energy consumptions incurred per rounds. Then, a novel particle swarm optimization variation (PSOV)‐based cluster head selection scheme was contributed to ensuring remarkable functions in dynamic settings of sensor networks 16 . This PSOV scheme incorporated the construction of collaborating multiple swarms using the charged and extended PSO population.…”

Section: Related Workmentioning

confidence: 99%

“…Then, a novel particle swarm optimization variation (PSOV)-based cluster head selection scheme was contributed to ensuring remarkable functions in dynamic settings of sensor networks. 16 This PSOV scheme incorporated the construction of collaborating multiple swarms using the charged and extended PSO population. This PSOV scheme uses the merits of connected dominating set for recalculation and updating of cluster heads when the topology of the sensor networks dynamically change.…”

Section: Related Workmentioning

confidence: 99%

Hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks

Tamilarasan

Lenin²,

Jayapandian

et al. 2021

Int J Communication

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Wireless sensor networks are significantly used for data sensing and aggregating dusts from a remote area environment in order to utilize them in a diversified number of engineering applications. The data transfer among the sensor nodes is attained through the inclusion of energy efficient routing protocols. These energy efficient routing necessitates optimal cluster head selection procedure for handling the challenge of energy consumption to extend the stability and lifetime in the sensor networks. The implementation of energy efficient routing is still complicated even when the process of clustering is enhanced through the cluster head selection. The majority of the existing cluster head selection schemes suffer from the issues of poor selection accuracy, increased computation, and duplicate nodes' selection. In this paper, hybrid shuffled frog leaping and improved biogeography-based optimization algorithm (HSFLBOA) for optimal cluster head selection is proposed for resolving issues that are common in cluster head selection schemes. This proposed HSFLBOA used the objective function that used the parameters of node energy, data packet transmission delay, cluster traffic density, and internode distance in the cluster. The simulation results of the proposed HSFLBOA is determined to be significant in achieving superior throughput and network energy compared to benchmarked metaheuristic optimal cluster head schemes.

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Presenting a New Clustering Algorithm by Combining Intelligent Bat and Chaotic Map Algorithms to Improve Energy Consumption in Wireless Sensor Network

Asadi

Mazinani

2018

Lecture Notes in Electrical Engineering

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A Teaching Learning Based Optimization Algorithm for Cluster Head Selection in Wireless Sensor Networks

Abstract: Abstract

Cited by 6 publications

References 29 publications

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

Hybrid shuffled frog leaping and improved biogeography‐based optimization algorithm for energy stability and network lifetime maximization in wireless sensor networks

Presenting a New Clustering Algorithm by Combining Intelligent Bat and Chaotic Map Algorithms to Improve Energy Consumption in Wireless Sensor Network

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