Fuzzy C-Means Based Hierarchical Routing Approach for Homogenous WSN

Mahboub, Aziz; En-Naimi, El Mokhtar; Arioua, Mounir; Barkouk, Hamid

doi:10.1007/978-3-319-74500-8_24

Cited by 1 publication

(3 citation statements)

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“…FCM is a clustering algorithm that is unsupervised as same as the k-means algorithm with the same cluster division purpose. Nonetheless, k-means is a hard set based algorithm, and FCM is an algorithm based on the non-crisps approach (all individuals are listed in two groups: 1 or 0) [30][31][32]. This algorithm works by assigning affiliation to each sensor node that corresponds to each cluster center.…”

Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

“…This process is based on the distance between the cluster center and the sensor node. Therefore, the closer the sensor node is to the cluster center, the stronger its membership in cluster center is [30][31][32][33]. The FCM algorithm represents an iterative optimization algorithm that minimizes the following objective function [33].…”

Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

“…The F-LEACH use three algorithms. Firstly, it starts using the subtractive clustering approach to evaluate the correct number of clusters [32]. The FCM algorithm is then implemented to form highly uniform clustering dispersion of nodes (segmentaion of network).…”

Section: Fuzzy C-means Based Hierarchical Routing Approach (F-leach)mentioning

confidence: 99%

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Performance evaluation of hierarchical clustering protocols with fuzzy C-means

Barkouk

En-Naimi

Mahboub

2021

IJECE

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The longevity of the network and the lack of resources are the main problems within the WSN. Minimizing energy dissipation and optimizing the lifespan of the WSN network are real challenges in the design of WSN routing protocols. Load balanced clustering increases the reliability of the system and enhances coordination between different nodes within the network. WSN is one of the main technologies dedicated to the detection, sensing, and monitoring of physical phenomena of the environment. For illustration, detection, and measurement of vibration, pressure, temperature, and sound. The WSN can be integrated into many domains, like street parking systems, smart roads, and industrial. This paper examines the efficiency of our two proposed clustering algorithms: Fuzzy C-means based hierarchical routing approach for homogeneous WSN (F-LEACH) and fuzzy distributed energy efficient clustering algorithm (F-DEEC) through a detailed comparison of WSN performance parameters such as the instability and stability duration, lifetime of the network, number of cluster heads per round and the number of alive nodes. The fuzzy C-means based on hierarchical routing approach is based on fuzzy C-means and low-energy adaptive clustering hierarchy (LEACH) protocol. The fuzzy distributed energy efficient clustering algorithm is based on fuzzy C-means and design of a distributed energy efficient clustering (DEEC) protocol. The technical capability of each protocol is measured according to the studied parameters.

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Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%

Section: Fuzzy C-means Clustering Algorithmmentioning

confidence: 99%