A Comparative Study on Prominent Strategies of Cluster Head Selection in Wireless Sensor Networks

Maratha, Priti; Kapil,

doi:10.1007/978-981-10-8797-4_39

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Many researchers examined the performance of KM and FCM by functioned a relative comparison between these algorithms based on execution time and accuracy of these clustering algorithms in other fields [3,4,10,12,13,28,29]. Based on [11] KM is superior in terms the total sum of distances for nodes and time of execution in comparison to FCM when applied these algorithms on three intrusion datasets which are: KDDCup99, NSLKDD, and GureKDD.…”

Section: Related Workmentioning

confidence: 99%

“…The CH possesses numerous functions in addition to sensing the environment such as; data gathering from all cluster members, and its conveyance to the main node termed as Base Station (BS), the conveyance of other CHs data to the next hop, and the fusion of the cluster data. Clustering approach is the most popular energy efficient technique which provides various advantages such as prolonging the network lifetime, scalability and enabling less delay, where it is considered as an advantage for both the lifespan and the scalability of a network [4,5]. In general, clustering algorithms are signified as the compilation of unsupervised classification methods that assigns objects into groups, or the partitioning of datasets into subsets known as clusters.…”

Section: Introductionmentioning

confidence: 99%

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Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

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The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

View full text Add to dashboard Cite

show abstract

“…The CH possesses numerous functions in addition to sensing the environment such as; data gathering from all cluster members, and its conveyance to the main node termed as base station (BS), the conveyance of other CHs data to the next hop, and the fusion TELKOMNIKA Telecommun Comput El Control  Improved fuzzy c-means algorithm based on a novel balanced clusters ... (Ali Abdul-hussian Hassan) 2895 of the cluster data. Clustering approach is the most popular energy efficient technique which provides various advantages such as prolonging the network lifetime, scalability and enabling less delay, where it is considered as an advantage for both the lifespan and the scalability of a network [5].…”

Section: Introductionmentioning

confidence: 99%

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

et al. 2020

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The clustering approach is considered as a vital method for many fields such as machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others. Similarly, it has great significance in wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor's battery, that is considered as a major challenge in the WSNs. Fuzzy c-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network. To overcome this problem, a new clustering method called FCM-CM has been proposed by improving the FCM algorithm to form balanced clusters for random nodes deployment. The improvement is conducted by integrating the FCM with a centralized mechanism (CM). The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clusters in addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.

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Linear optimization and fuzzy-based clustering for WSNs assisted internet of things

Maratha

Gupta

2022

Multimed Tools Appl

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A Comparative Study on Prominent Strategies of Cluster Head Selection in Wireless Sensor Networks

Cited by 4 publications

References 17 publications

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

Linear optimization and fuzzy-based clustering for WSNs assisted internet of things

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