Swarm Intelligence–Based Energy‐Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications

Devika, G; Ramesh, D.; Karegowda, Asha Gowda

doi:10.1002/9781119778868.ch12

Cited by 8 publications

(1 citation statement)

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“…They presented a hybrid algorithm of Moth flame optimization (MFO) and genetics for clustering WSNs to alleviate energy usage, and finally, they evaluated the efficiency of their designed model with other methods such as LEACH, HEED, and ABC. In [ 19 ] , the authors tried to scrutinize the performance of swarm-based algorithms. In this article, the application of 60 meta-heuristic algorithms in WSN clustering was implemented and compared with each other.…”

Section: Related Workmentioning

confidence: 99%

Reducing Energy Consumption and Clustering in Wireless Sensor Networks using an Improved Discrete Gorilla Troops Optimization Algorithm with Fuzzy Rules

Rashid

2023

JZS

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Among the most common and extensively operated methods in wireless sensor networks (WSN) to increase the efficiency and performance of WSN is network clustering because, through this solution, data is transmitted through the closest possible path. In the clustering method, a portion of the nodes in the network become cluster heads, then a cluster is formed by joining the nodes close to the cluster head. However, the main and important problem in this issue is preventing the creation of unbalanced clusters since the spreading of cluster heads in the network can be unequal. In this paper, we have presented an algorithm based on the discrete Gorilla Troops Optimizer (DGTOA) algorithm and K-Means with a fuzzy clustering approach. In this model, first, several cluster heads are chosen by applying the discrete DGTOA algorithm, and then the output of the DGTOA algorithm is given as initial points and coordinates in K-means. Finally, the cluster head is utilized to wield the fuzzy system. Also, different criteria and graphs were used to compare the proposed model, and the obtained results were measured with other methods, and the obtained outcomes indicate the high performance of the proposed model.

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Section: Related Workmentioning

confidence: 99%