Cluster Formation Algorithm in WSNs to Optimize the Energy Consumption Using Self-Organizing Map

Nayak, Padmalaya; Swetha, G.; Kaushal, Priyanka; Padhan, D. G.

doi:10.1007/978-981-16-2919-8_2

Cited by 7 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several stages in the process of identifying the health of betel leaf by applying the SOM (Self Organizing Maps) method, namely the preprocessing, feature extraction, and classification stages. The software used in the analysis of betel leaf image data is Matlab software (Nayak et al, 2022). The stages carried out in this research are as follows:…”

Section: Methodsmentioning

confidence: 99%

Health Detection of Betal Leaves Using Self-Organizing Map and Thresholding Algorithm

Mulyana¹,

Saepudin²,

Yel³

2022

JAETS

View full text Add to dashboard Cite

Betel leaf is one of the plants that is widely used as a natural or traditional medicine by the community, natural treatment with the use of plants is relatively safer. But there is a problem when we choose healthy betel leaves because of our mistakes in choosing which betel leaves are healthy and which are not. With this research the authors aim to detect healthy and sick betel leaves using data collection. Feature extraction used is the value of Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV) to get the characteristics of the color image. Then the results of the feature extraction are used to classify the health of green betel leaves using the Self-Organizing Maps method. The green betel leaf data used is 1500 images for train data and 450 images for testing data are image test data, test data that produces an evaluation value with an accuracy value of 97.20% on the Self-Organizing Maps method.

show abstract

Section: Methodsmentioning

confidence: 99%

Health Detection of Betal Leaves Using Self-Organizing Map and Thresholding Algorithm

Mulyana¹,

Saepudin²,

Yel³

2022

JAETS

View full text Add to dashboard Cite

show abstract

“…Researchers have explored various extension protocols to enhance LEACH clustering, focusing on factors such as distance and energy optimization [18]. FL-LEACH employs fuzzy logic to choose super cluster heads (SCHs) to enhance data transmission efficiency [19]. Furthermore, the distributed fuzzy clustering algorithm employs fuzzy logic controllers (FLCs) to organize WSNs into clusters, enhancing network lifetime through multi-hop routing of data packets [20].…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Cluster Head Selection in Wireless Sensor Networks

AlZyoud,

Tarawneh,

Almaghthawi

et al. 2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

The proliferation of mobile devices and the spread of IoT devices have increased the tendency to use wireless sensor networks (WSNs), especially since the implementation of the 5G communication system has begun in most countries. This type of network does not require any infrastructure or additional cost, making it a good alternative for use in disasters, environmental monitoring, military, and rescue operations. However, WSNs suffer from some limitations, such as mobility and battery lifetime. Significant research has been conducted to overcome the limitation of battery lifetime by developing routing methods and reducing the required communications among wireless mobile nodes. In this research, we utilize the low-energy adaptive clustering hierarchy (LEACH) concept to minimize communication between the nodes and the base station. A new approach has been developed to form clusters in WSN nodes and select the optimal cluster head by facilitating the election of a new cluster head (CH). When the current cluster head’s energy depletes, a new one is selected, ensuring continuous operation. The simulation results demonstrate that the proposed algorithm outperforms the existing LEACH clustering algorithm in terms of energy consumption, packet delivery ratio (PDR), and latency time.

show abstract

“…Nayak et al 125 employed SOM to partition network nodes into clusters. The cluster sizes were varied using the K‐means algorithm to determine the ideal number of clusters.…”

Section: Solutionsmentioning

confidence: 99%

Machine learning‐based clustering protocols for Internet of Things networks: An overview

Merah

Aliouat

Harbi

et al. 2023

Int J Communication

View full text Add to dashboard Cite

Summary The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML‐based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology‐centered ML‐based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML‐based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.

show abstract

Cluster Formation Algorithm in WSNs to Optimize the Energy Consumption Using Self-Organizing Map

Cited by 7 publications

References 13 publications

Health Detection of Betal Leaves Using Self-Organizing Map and Thresholding Algorithm

Health Detection of Betal Leaves Using Self-Organizing Map and Thresholding Algorithm

A New Approach for Cluster Head Selection in Wireless Sensor Networks

Machine learning‐based clustering protocols for Internet of Things networks: An overview

Contact Info

Product

Resources

About