Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network

Kumar, Bhawnesh; Tiwari, Umesh; Kumar, Santosh

doi:10.1109/pdgc50313.2020.9315853

Cited by 14 publications

(6 citation statements)

References 6 publications

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“…Some choose the CH based on the back propagation technology of artificial neural network to improve the network energy efficiency and robustness [8]. Others choose Energy Efficient Quad Clustering to improve the performance of wireless sensor network in terms of network lifetime [9]. However, these methods are complex, and the selection time of CH is long.…”

Section: Introductionmentioning

confidence: 99%

A Competition-Based Unequal Clustering Multihop Approach for Wireless Sensor Networks

Liu

et al. 2021

Security and Communication Networks

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The energy efficiency and stability of wireless sensor networks (WSNs) have always been a hot issue in the research. Clustering is a typical architecture for WSNs, and cluster heads (CHs) play a vital role. Unreasonable CH selection causes a lot of energy consumption. In this paper, we propose a competition-based unequal clustering multihop approach (CUCMA). CHs are selected by competition. First, the cluster radius (CR) of a node is calculated according to the distance to base station (BS). Then, CR is resized based on the number of around nodes. Only the nodes with high residual energy and appropriate distances to the selected CHs maybe become CHs, which are usually closer to the surrounding nodes. CUCMA and four related approaches are simulated in different scenarios. The results are analyzed, and it is proved that CUCMA balances the energy consumption of the CHs and reduces the energy consumption of the whole networks, thus leading to prolong the lifetime of WSNs.

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Section: Introductionmentioning

confidence: 99%

A Competition-Based Unequal Clustering Multihop Approach for Wireless Sensor Networks

Liu

et al. 2021

Security and Communication Networks

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“…Several methods can be employed to determine the ideal or effective number of clusters K. These methods include the gap statistic, average silhouette, and elbow techniques. The elbow technique is a valuable strategy in this context and is given by [27]:…”

Section: Network Cluster Formation Using K-means Clusteringmentioning

confidence: 99%

An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy

Saadallah,

Alabady

2024

JTIT

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This paper introduces a method that combines the K-means clustering genetic algorithm (GA) and Lempel-Ziv-Welch (LZW) compression techniques to enhance the efficiency of data aggregation in wireless sensor networks (WSNs). The main goal of this research is to reduce energy consumption, improve network scalability, and enhance data aggregation accuracy. Additionally, the GA technique is employed to optimize the cluster formation process by selecting the cluster heads, while LZW compresses aggregated data to reduce transmission overhead. To further optimize network traffic, scheduling mechanisms are introduced that contribute to packets being transmitted from sensors to cluster heads. The findings of this study will contribute to advancing packet scheduling mechanisms for data aggregation in WSNs in order to reduce the number of packets from sensors to cluster heads. Simulation results confirm the system's effectiveness compared to other compression methods and non-compression scenarios relied upon in LEACH, M-LEACH, multi-hop LEACH, and sLEACH approaches.

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“…This type of clustering [38] adapts K-Means clustering algorithm. The K-Means clustering is one of the unsupervised Clustering techniques.…”

Section: Quad Clustering Based On K-means Algorithmmentioning

confidence: 99%

“…Comparison of Clustering TechniquesQuad Clustering based on K-Means Algorithm[38] • Reduces the consumption of energy • It do not support multiple clusters.…”

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confidence: 99%

Novel Clustering Techniques in Wireless Sensor Networks – A Survey

Swetha Priya,

Sridevi

2023

Int. j. electr. comput. eng. syst. (Online)

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A study of Wireless Sensor Networks has been growing tremendously these days. Wireless Sensor Networks play a major role in various fields ranging from smart homes to health care. WSN’s operate independently in remote places. Because of tiny size of the nodes in such type of networks, they have a limited number of resources in terms of energy and power. Basically, sensor networks can be classified into flat and cluster based Wireless Sensor Networks. But, Clustering based Sensor Networks play a major role in reducing the energy consumption in Wireless Sensor Networks. Clustering also focuses on solving the No.s that arise during transmission of data. Clustering will group nodes into clusters and elects Cluster Heads for all clusters in the network. Then the nodes sense data and send that data to cluster head where the aggregation of data will take place. This paper focuses on various novel clustering techniques that improve the network’s lifetime.

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Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network

Cited by 14 publications

References 6 publications

A Competition-Based Unequal Clustering Multihop Approach for Wireless Sensor Networks

A Competition-Based Unequal Clustering Multihop Approach for Wireless Sensor Networks

An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy

Novel Clustering Techniques in Wireless Sensor Networks – A Survey

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