Optimization of clustering process for WSN with hybrid harmony search and K-means algorithm

Raval, Dharmanshu; Raval, Gaurang; Valiveti, Sharada

doi:10.1109/icrtit.2016.7569584

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…K‐means is used to create the cluster with the appropriate sensor network [22, 42]. It falls under the category of unsupervised learning.…”

Section: Proposed Protocolmentioning

confidence: 99%

“…Clustering reduces communication overhead and power consumption, which increases network lifetime [20,21]. According to the network layout, routing in WSN can be classified as flat routing, hierarchical routing, or location routing [22,23], as shown in Figure 2. This work is aimed at one of the hierarchal routing protocol categories that are used to improve the performance of WSNs through modifying the chain-based routing protocols to address single chain communication, where sensor nodes use more energy and die more quickly.…”

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confidence: 99%

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Energy‐efficient routing protocol developed for internet of things networks

Ahmmad

Alabady

2022

IET Quantum Communication

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Wireless Sensor Networks (WSNs) due to their numerous applications have become a significant research topic in recent years, which include monitoring, tracking/detection, medical, military surveillance, and industrial. Due to the small sensors' difficulty in being easily recharged after random deployment, energy consumption is a challenging research problem for WSNs in general. One popular scenario for reducing energy consumption for WSNs is to use cluster‐based technology to reduce sensor node communication distance. Along with focusing on the Chain Cluster Based routing protocol classes. Initially, the Calinski Harabasz approach is utilized to find the optimum number of clusters. This modification will take place for two stages that pass via improving techniques of enhancing the Improved Energy‐Efficient PEGASIS‐Based (IEEPB) protocol to achieve the main goal of this study. The network lifetime was then extended by using the K‐means algorithm. As a result, rather than using a single long path, data is transferred over shorter parallel lines. The protocol is simulated with the MatlabR2015b simulator, which produces clear and effective simulation results, particularly in terms of energy savings. The outcome of the simulation results shows that the Improved energy‐efficient PEGASIS‐based routing protocol‐ KMeans optimisation (Improved EEPB‐ K‐means Optimisation) protocol outperforms the Low‐energy adaptive clustering hierarchy, Power‐efficient gathering in sensor information systems, IEEPB, and MIEEPB protocols.

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“…K‐means is used to create the cluster with the appropriate sensor network [22, 42]. It falls under the category of unsupervised learning.…”

Section: Proposed Protocolmentioning

confidence: 99%

mentioning

confidence: 99%

Energy‐efficient routing protocol developed for internet of things networks

Ahmmad

Alabady

2022

IET Quantum Communication

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“…Nazeer, Sebastian, and Kumar [145] presented HSKH-harmony search K-means hybrid for gene expression clustering, which produced a more accurate gene clustering solution. Raval, Raval, and Valiveti [146] proposed a combination of HS and K-means for optimizing wireless sensor network clustering. The HS was used to generate the initial solution, which is then fed into the K-means algorithm for a more precise solution.…”

Section: Harmony Searchmentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

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K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

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“…The harmony search algorithm (HSA) [ 27 ] is music-based meta-heuristic optimization method which is analogous with the music improvisation process where musician continue to polish the pitches in order to obtain a better harmony. Raval [ 28 ] applied it for minimizing the intra-cluster distance and optimizing the energy consumption of the network. IHSCR [ 29 ] was an energy-efficient clustering and routing based on the improved harmony search algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Balanced Clustering Protocol Based on an Improved CFSFDP Algorithm for Wireless Sensor Networks

Zhang

Liu

2018

Sensors

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Clustering, as an essential part in an hierarchy protocol that can prolong the network lifetime, is influenced by the cluster head selection and clustering scheme. A new clustering algorithm called clustering by fast search and finding of density peaks (CFSFDP) based on local density and distance is implementable and efficient. In this paper, we combine this clustering algorithm with a hierarchy protocol in wireless sensor networks (WSNs). However, energy consumption in each round is unbalanced only considering these two variables during the clustering phase, which leads to the early death of the first node. In order to solve this problem, we take residual energy into consideration in our improved CFSFDP-E (energy) algorithm so as to ultimately balance the energy consumption of the network. We analyze different forms of energy and choose a dynamic threshold for each round in the CFSFDP-E algorithm. Simulation results demonstrate that the proposed approach can not only postpone the death of the first node by almost 50% compared to LEACH, but that it also outperforms several related protocols with respect to energy efficiency.

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Optimization of clustering process for WSN with hybrid harmony search and K-means algorithm

Cited by 14 publications

References 10 publications

Energy‐efficient routing protocol developed for internet of things networks

Energy‐efficient routing protocol developed for internet of things networks

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

An Energy-Balanced Clustering Protocol Based on an Improved CFSFDP Algorithm for Wireless Sensor Networks

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