A Hybrid Cluster and Chain-Based Routing Protocol for Lifetime Improvement in WSN

Hadjila, Mourad; Guyennet, Hervé; Feham, Mohammed

doi:10.1007/978-3-319-13174-0_20

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…In [99,100], the proposed EANOC aims to enhance the network lifetime. It is an improvised version of the 'hybrid weightbased clustering algorithm' [101] with focus on neighbour node discovery.…”

Section: Energy Aware Neighbour Oriented Clustering (Eanoc)mentioning

confidence: 99%

Survey on fault tolerance‐based clustering evolution in WSN

Mohapatra

Rath

2020

IET Networks

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Ongoing research on remote communication and sensing essentially made accessible the flexible technology called 'wireless sensor network (WSN)', which is independent, self-organising and self-healing in nature. The WSN comprises an extensive number of homogeneous or heterogeneous sensor nodes that may be deployed in a uniform or non-uniform manner in a targeted zone. The organisation of WSN can be stretched out from residential to a harsh and hostile environment. The manageability of WSN is very subjective to an efficient adaptation to the failure situations. In this line of thought, clustering has been proven as an efficient strategy for prolonging the sensor network lifetime and selecting the correct topological structure for the sensor network. The authors' first present a survey of existing survey works from 2002 to 2019 and then present a tutorial on existing fault-tolerant protocols with a comparative analysis. The target audience of this work is novice researchers in the field of WSN to get a preliminary idea about clustering and its objective.

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Section: Energy Aware Neighbour Oriented Clustering (Eanoc)mentioning

confidence: 99%

Survey on fault tolerance‐based clustering evolution in WSN

Mohapatra

Rath

2020

IET Networks

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“…Likewise, the FCM algorithm is widely used by researchers to construct balanced clusters size. Since it has the ability to determine the cluster's centroid such as KM, that helps in the optimization of the clusters according to the minimization of the space between the sensor node and the cluster centroid [22]. Alia [23] a Decentralized Fuzzy Clustering Protocol, named DCFP was suggested.…”

Section: Related Workmentioning

confidence: 99%

“…The utilization of FCM in WSNs assists in changing the LEACH protocol parameters during the implementation. Through Hadjila et al [22] suggested a duo of algorithms utilizing a method which integrates the fFuzzy C-Means Algorithm and the ant colony optimization in the construction of the clusters, and the management of the data transference within the network. Firstly, Fuzzy C-Means clustering Algorithm is utilized in the formation of a predetermined amount of clusters.…”

Section: Related Workmentioning

confidence: 99%

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

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

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The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.

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“…A new plan was recommended for choosing strong nodes proximate to the sink for straight data transmissions. Hadjila et al [17] suggested a duo of algorithms utilizing a method which integrates the fuzzy c-means algorithm and the ant colony optimization in the construction of the clusters, and the management of the data transference within the network. Firstly, fuzzy c-means clustering algorithm is utilized in the formation of a predetermined number of clusters.…”

Section: Related Workmentioning

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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A Hybrid Cluster and Chain-Based Routing Protocol for Lifetime Improvement in WSN

Cited by 7 publications

References 23 publications

Survey on fault tolerance‐based clustering evolution in WSN

Survey on fault tolerance‐based clustering evolution in WSN

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

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

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