A novel centralized clustering approach based on K-means algorithm for wireless sensor network

Echoukairi, Hassan; Kada, Amine; Bouragba, Khalid; Ouzzif, Mohammed

doi:10.1109/sai.2017.8252252

Cited by 18 publications

(14 citation statements)

References 13 publications

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“…For global clustering, time domain and frequency domain indexes are introduced to reduce data dimensionality. According to indexes such as Davies-Bouldin (DB) index, classification accuracy, iteration times, and calculation time, several classic algorithms are introduced to test the performance of the two-layer DCA proposed above, including the centralized KMC algorithm (K) [ 21 ], the centralized AP algorithm (AP) [ 22 ], the K-means-AP clustering algorithm based on time-domain features (TK-AP), and the distributed K-means-AP clustering algorithm based on optimum combined features (CK-AP). The proposed algorithm’s effectiveness is tested by comparing various indexes.…”

Section: Methodsmentioning

confidence: 99%

The role of clustering algorithm-based big data processing in information economy development

2021

PLoS ONE

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The purposes are to evaluate the Distributed Clustering Algorithm (DCA) applicability in the power system’s big data processing and find the information economic dispatch strategy suitable for new energy consumption in power systems. A two-layer DCA algorithm is proposed based on K-Means Clustering (KMC) and Affinity Propagation (AP) clustering algorithms. Then the incentive Demand Response (DR) is introduced, and the DR flexibility of the user side is analyzed. Finally, the day-ahead dispatch and real-time dispatch schemes are combined, and a multi-period information economic dispatch model is constructed. The algorithm performance is analyzed according to case analyses of new energy consumption. Results demonstrate that the two-layer DCA’s calculation time is 5.23s only, the number of iterations is small, and the classification accuracy rate reaches 0.991. Case 2 corresponding to the proposed model can consume the new energy, and the income of the aggregator can be maximized. In short, the multi-period information economic dispatch model can consume the new energy and meet the DR of the user side.

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Section: Methodsmentioning

confidence: 99%

The role of clustering algorithm-based big data processing in information economy development

2021

PLoS ONE

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“…In 2017, Echoukairi et al [23] discussed WSN in terms of centralized clustering approach. It is based on k-means method.…”

Section: Literature Reviewmentioning

confidence: 99%

Computational analysis of clustering techniques for the efficient cluster head selection

Khandelwal¹,

Jain²

2019

IJATEE

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In the current era there are lots of work have been carried out in the direction of cluster heads (CHs) selection in wireless sensor network (WSN). Despite of these works there is still need of improvement in the suggested methods and approach. This paper provides a computational analysis of the related method published of clustering techniques for the efficient cluster head selection and based on the other approaches. In general k-means, fuzzy c-means (FCM) and hierarchical clustering have been considered for the analysis along with the computational measures. This study explores the analytical and experimental discussion and the trends for the efficient cluster head selection.

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“…It is required to use the unsupervised classification of nodes to create a cluster. k-means is one the unsupervised learning to address clustering issue [6]. The k-means use a predefined number of cluster (assume k clusters) in the first step.…”

Section: B K-means Technique Based On Distance Clusteringmentioning

confidence: 99%

“…Moreover, Ref. [6] proposed a LEACH-Centralized protocol to minimize the disadvantages of the LEACH by applying the k-means algorithm. The proposed system was implemented by partitioning the network into several clusters in the first step using k-means method.…”

Section: Introductionmentioning

confidence: 99%

An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using k-Means Clustering

Azhar

Zainudin

Kristalina

et al. 2018

CommIT (Communication and Information Technology) Journal

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Wireless Sensor Network (WSN) is a network consisting of several sensor nodes that communicate with each other and work together to collect data from the surrounding environment. One of the WSN problems is the limited available power. Therefore, nodes on WSN need to communicate by using a cluster-based routing protocol. To solve this, the researchers propose a node grouping based on distance by using k-means clustering with a hardware implementation. Cluster formation and member node selection are performed based on the nearest device of the sensor node to the cluster head. The k-means algorithm utilizes Euclidean distance as the main grouping nodes parameter obtained from the conversion of the Received Signal Strength Indication (RSSI) into the distance estimation between nodes. RSSI as the parameter of nearest neighbor nodes uses lognormal shadowing channel modeling method that can be used to get the path loss exponent in an observation area. The estimated distance in the observation area has 27.9% error. The average time required for grouping is 58.54 s. Meanwhile, the average time used to retrieve coordinate data on each cluster to the database is 45.54 s. In the system, the most time-consuming process is the PAN ID change process with an average time of 14.20 s for each change of PAN ID. The grouping nodes in WSN using k-means clustering algorithm can improve the power efficiency by 6.5%.

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A novel centralized clustering approach based on K-means algorithm for wireless sensor network

Cited by 18 publications

References 13 publications

The role of clustering algorithm-based big data processing in information economy development

The role of clustering algorithm-based big data processing in information economy development

Computational analysis of clustering techniques for the efficient cluster head selection

An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using k-Means Clustering

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