Performance Analysis of LEACH with Machine Learning Algorithms in Wireless Sensor Networks

Randhawa, Sukhchandan; Jain, Sushma

doi:10.5120/ijca2016910988

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…LEACH is a hierarchical protocol in which nodes transmit data to CHs, and then they forward data to the base station (sink) [ 16 , 30 ]. The main idea of the LEACH protocol is to divide the whole WSN into several clusters [ 3 , 13 , 19 , 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…The next step is to measure the Euclidean distance between each node and the centers of the cluster; the smallest distance is chosen to include this node in the nearest cluster center [ 2 , 7 , 25 , 26 , 27 , 34 , 37 ]. After all the nodes are grouped, the algorithm determines the new center of gravity for each cluster at each round [ 30 ]. The algorithm stops when the groups become stable [ 5 , 7 , 18 , 23 , 24 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…The K-LEACH protocol is divided into several rounds, and each round includes a cluster formation phase and a stable state round [1,11,27,33,36]. Using K-means as a clustering strategy can minimize overhead during the CHs' re-election [5,22,30]. CH election in LEACH is based according to a certain energy threshold value [31].…”

Section: Leach-based K-means Algorithmmentioning

confidence: 99%

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Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring

Tadros

Shehata

Mokhtar

2023

Sensors

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Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs’ lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Leach-based K-means Algorithmmentioning

confidence: 99%

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Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring

Tadros

Shehata

Mokhtar

2023

Sensors

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“…Building a Wireless Sensor Network (WSN) begins with installing the network's Base Station (BS) and Sensor Nodes (SNs). Select the area for the network and place SNs there [26]. Make sure the SNs nodes have all the gadgets and means of communication they need.…”

Section: Installation Of Network Nodesmentioning

confidence: 99%

Wireless Sensors Networks Energy Optimization using LEACH and ANNs

Qamar¹,

Haq²

2023

Preprint

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Applications for Wireless Sensor Networks (WSNs) range from monitoring the environment to automating factories. However, sustained and effective functioning is made more difficult by Sensor Nodes (SNs) limited energy supplies in which optimization is the main issue. So with the aim of increasing the lifespan by decreasing the energy consumption of WSN, Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol with Deep Learning (DL) algorithm is analyzed in this paper. LEACH is a hierarchical mechanism that elects Cluster Heads (CHs) and regularly rotates their positions in order to allocate energy use effectively by using the same amount of energy. However, Deep Learning (DL) method is used to further improve energy optimization. In many applications, the types of Deep Learning methods like Artificial Neural Networks (ANNs) have shown to be very useful. Using this method, WSNs may make more efficient decisions that reduce energy consumption. Data aggregation, duty cycling, and transmission protocols may all be optimized by Deep Learning model's ability to recognize patterns and forecast network behavior. This results in lower energy consumption, a longer lifespan for the network, and better overall performance.

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“…It did not improve the QoS of the network. [9] In this paper, researchers implemented and discussed the K-Means algorithm and Gauss algorithms in LEACH.…”

Section: Studymentioning

confidence: 99%

Enhancing QoS and Residual Energy by Using of Grid-Size Clustering, K-Means, and TSP Algorithms With MDC in LEACH Protocol

2022

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Some recent researches have shown that the energy consumption problem caused by data collection in a wireless sensor network (WSN) based on a static data collector is a main threat to the network lifetime. However, with the progress of the mobile terminal technology, the implementation of mobile data collectors (MDCs) has become more popular in large-scale WSNs, but it remains a big problem to improve the Quality of Service (QoS) criteria and minimize the energy consumption at the same time. However, most existing systems based on MDCs do not successfully strike a balance between routing energy consumption and QoS. In addition, most WSN protocols fail to maintain their impact when the network topology changes. Thus, for a dynamic WSN, it is important to support an intelligent MDC to continue data propagation despite the inevitable changes in the WSN topology. Considering all the above challenges, we propose a new intelligent MDC based on the traveling salesman problem (TSP) to determine the optimal path traveled by the MDC for energy efficiency and latency. Specifically, our proposed Mobile Data Collectors-Traveling Salesman Problem-Low Energy Adaptive Clustering Hierarchy-K-Means (MDC-TSP-LEACH-K) protocol uses K-Means and Grid clustering algorithm to decrease energy consumption in the cluster head (CH) election phase. Additionally, MDC is utilized as an intermediate between CH and the sink to further enhance the QoS of WSNs, to reduce delays while collecting data, and improve the transmission phase of the LEACH protocol.INDEX TERMS Energy consumption, large-scale wireless sensor networks, optimal path, QoS.

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Performance Analysis of LEACH with Machine Learning Algorithms in Wireless Sensor Networks

Cited by 11 publications

References 11 publications

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring

Wireless Sensors Networks Energy Optimization using LEACH and ANNs

Enhancing QoS and Residual Energy by Using of Grid-Size Clustering, K-Means, and TSP Algorithms With MDC in LEACH Protocol

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