A Decentralized Fuzzy C-Means-Based Energy-Efficient Routing Protocol for Wireless Sensor Networks

Alia, Osama Moh’d

doi:10.1155/2014/647281

Cited by 34 publications

(13 citation statements)

References 16 publications

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“…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. The construction procedure of the framework for a particular WSNs is conducted one off at the starting of the protocol at a base station, that persists in its unaltered state transcending the entirety of the lifespan of the the network.…”

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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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

View full text Add to dashboard Cite

show abstract

“…Secondly, the ant colony optimization (ACO) algorithm was applied in the formation of a local minimal chain in individual clusters. Through Alia's work [18], a decentralized fuzzy clustering protocol (DCFP) was suggested.…”

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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“…, when the sink is far away from the nodes. Multi-criteria objective function performs a decentralization of the clustering protocol [ 52 , 53 ] different from LEACH. CHs are elected in every round, but the election of the CHs again and again also consumes node energy.…”

Section: Related Workmentioning

confidence: 99%

A Novel Scheme for an Energy Efficient Internet of Things Based on Wireless Sensor Networks

Rani

Talwar²,

Malhotra³

et al. 2015

Sensors

190

106

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One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.

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A Decentralized Fuzzy C-Means-Based Energy-Efficient Routing Protocol for Wireless Sensor Networks

Cited by 34 publications

References 16 publications

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

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

A Novel Scheme for an Energy Efficient Internet of Things Based on Wireless Sensor Networks

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