Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in  wireless sensor networks

Hassan, Ali; Shah, Wahidah Md; Othman, Mohd Fairuz Iskandar; Hassan, Hayder Abdul Hussien

doi:10.11591/ijece.v10i2.pp1515-1523

Cited by 23 publications

(20 citation statements)

References 30 publications

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“…To terminate the algorithm, we use the condition Uij(t)− Uij(t− 1) < ε, where t is the current iteration, and ε is a very small number close to zero (e.g., 0.001). On certain occasions, FCM produces unbalanced clusters because of the nature of the random deployment of sensor nodes in the monitoring area [36], as shown in Figure 2. This situation leads to unbalanced energy consumption for nodes, which adversely affects the network lifetime [10] [15] [26].…”

Section: Fcm Algorithm Overviewmentioning

confidence: 99%

An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

et al. 2020

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A wireless sensor network (WSN) is an important part of the Internet of Things (IoT). However, sensor nodes of a WSN-based IoT network are constraining with the energy resources. A clustering protocol provides an efficient solution to ensure energy saving of nodes and prolong the network lifetime by organizing nodes into clusters to reduce the transmission distance between the sensor nodes and base station (BS). However, existing clustering protocols suffer from issues concerning the clustering structure that adversely affects the performance of these protocols. In this study, we propose an improved energyefficient clustering protocol (IEECP) to prolong the lifetime of the WSN-based IoT. The proposed IEECP consists of three sequential parts. First, an optimal number of clusters is determined for the overlapping balanced clusters. Then, the balancedstatic clusters are formed on the basis of a modified fuzzy C-means algorithm by combining this algorithm with a mechanism to reduce and balance the energy consumption of the sensor nodes. Lastly, cluster heads (CHs) are selected in optimal locations with rotation of the CH function among members of the cluster based on a new CH selection-rotation algorithm by integrating a back-off timer mechanism for CH selection and rotation mechanism for CH rotation. In particular, the proposed protocol reduces and balances the energy consumption of nodes by improving the clustering structure, where IEECP is suitable for networks that require a long lifetime. The evaluation results prove that the IEECP performs better than existing protocols.

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Section: Fcm Algorithm Overviewmentioning

confidence: 99%

An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

et al. 2020

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“…In this study, we utilize four parameters together, which had used in our previous work [26], where the dependency on measuring the size among the clusters alone is insufficient as a unique evaluation parameter for the consideration of this network to have balanced clusters. For that reason, this study relies on a set of parameters to evaluate the proposed algorithm.…”

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

et al. 2020

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The clustering approach is considered as a vital method for many fields such as machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others. Similarly, it has great significance in wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor's battery, that is considered as a major challenge in the WSNs. Fuzzy c-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network. To overcome this problem, a new clustering method called FCM-CM has been proposed by improving the FCM algorithm to form balanced clusters for random nodes deployment. The improvement is conducted by integrating the FCM with a centralized mechanism (CM). The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clusters in addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.

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“…Fuzzy C-Means clustering is a popular method used in fuzzy clustering [8]. Fuzzy C-Means clustering is a distance-based clustering that applies the concept of fuzzy logic [9]. The clustering process goes hand in hand with the iteration process to minimize the objective function [3] [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Distance Functions Study in Fuzzy C-Means Core and Reduct Clustering

Eliyanto

Surono

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Fuzzy clustering aims to produce clusters that take into account the possible membership of each dataset point in a particular cluster. Fuzzy C-Means Clustering Core and Reduct is a fuzzy clustering method is a Fuzzy C-Means Clustering method that has been optimized using the reduction of Core and Reduct dimensions. The method studied is highly dependent on the distance function used. As a further in-depth study, this study was compiled to see the performance of the Fuzzy C-Means Clustering Core and Reduct using various distance functions. We aim to see how consistent the results of this method are across various distance functions and find the best distance function. The seven distance functions are applied to the same dataset. The seven distances are the Euclidean, Manhattan, Minkowski, Chebyshev, Minkowski-Chebyshev, Canberra, and Averages distances. We use UCI Machine Learning datasets for this research. The quality of the clustering results is compared through several measures. Accuracy, Silhouette score, and Davies Bouldin Index are used as internal measurements. The results of Fuzzy C-Means Core and Reduct clustering on all distance functions have significantly decreased computational load. Accuracy and purity values can be maintained with values above 80%. There was an increase in the value of the Silhouette Coefficient Score and a decrease in the Davies Bouldin Index after the application of dimension reduction. This means the quality of the clustering results can be maintained. The distance with the best evaluation result is the Euclidean distance. This method runs consistently across all tested distance functions.

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Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Cited by 23 publications

References 30 publications

An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

An Improved Energy-Efficient Clustering Protocol to Prolong the Lifetime of the WSN-Based IoT

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

Distance Functions Study in Fuzzy C-Means Core and Reduct Clustering

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