N-LEACH, a balanced cost cluster-heads selection algorithm for Wireless Sensor Network

Tripathi, Rajiv K.; Singh, Yatindra Nath; Verma, Nishchal K.

doi:10.1109/ncc.2012.6176825

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…N-LEACH [17] improves LEACH by giving some nodes more chances to compete for cluster-heads. For the nodes having served as a cluster-head in the fixed number of rounds, if the total number of their own member nodes is less than the prescribed number, they are allowed to compete for cluster-heads.…”

Section: Related Workmentioning

confidence: 99%

A Convergent Algorithm for Energy-Balanced Cluster-Heads Selection in Wireless Sensor Networks

Chen

2014

International Journal of Distributed Sensor Networks

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Due to the limited energy of sensor nodes, it is a research goal that the lifetime of sensor networks is prolonged by transmitting the sensed data to the base station in an energy-saving way. Previous algorithms aim at reducing the average energy consumption rate to extend the network lifetime. However, some nodes sometimes may be served as the cluster-head too many times to conserve their energy, resulting in reduced network lifetime. Thus, the large deviation of network lifetime makes these algorithms impractical. This paper proposes a new clustering algorithm which not only reduces the average energy consumption rate, but also converges the residual energies of all nodes on a small interval. Based on the two-region cluster-heads selection mechanism, the coordinator adaptively adjusts the far-near regions to converge the energies of all nodes on a small interval. With the exclusion-circle of clusterheads, cluster-heads can be distributed evenly in a spatial respect for each round, resulting in reduced energy consumption. The simulation results show that the proposed algorithm not only makes cluster-heads distribute evenly in a spatial respect but also converges the residual energies of all nodes on a small interval, resulting in extending the network lifetime significantly and stably.

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Section: Related Workmentioning

confidence: 99%

A Convergent Algorithm for Energy-Balanced Cluster-Heads Selection in Wireless Sensor Networks

Chen

2014

International Journal of Distributed Sensor Networks

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“…Among the large number of clustering algorithms for sensor networks proposed in the literature, such as LCA (Baker and Ephremides, 1981), CLUBS (Nagpal and Coore, 1998), EEHC (Bandyopadhyay and Coyle, 2003) -all having a linear convergence rateor LEACH (Heinzelman et al, 2002), HEED (Younis and Fahmy, 2004), EECPL (Bajaber and Awan, 2010), N-LEACH (Tripathi et al, 2012) -with a constant convergence time -our proposed cluster topology is based on the DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm (Sander et al, 1998), a simple and widely used density-based clustering algorithm.…”

Section: Motivationmentioning

confidence: 99%

A Clustering Topology for Wireless Sensor Networks - New Semantics over Network Topology

Cotofrei¹,

Calistru²,

Stoffel

2013

Proceedings of the 2nd International Conference on Data Technologies and Applications

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Sensor networks are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical parameters in real time. Given the hardware limitations and physical environment in which the sensors must operate, along with frequent changes of network topology, algorithms and protocols must be designed to provide a robust and energy efficient communications mechanism. With a view to addressing these constraints, this paper proposes a routing technique that is based on density based spatial clustering of applications with noise (DBSCAN) algorithm. This technique reveals several network topology semantics, enables the splitting of sensors responsibilities (communication/routing and sensing/monitoring), reduces the level of energy wasted on sending messages through the network by data aggregation only in cluster-head nodes and last but not the least, brings along very good results prolonging the network lifetime.

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“…A number of significant clustering techniques are inspired and based on LEACH and have been discussed in this paper. In Manjeshwar et al (2001), Younis and Fahmy (2004a, 2004b), Anastasi et al (2009), Kumar et al (2009), Tripathi et al (2012), Aderohunmu et al (2011) and Shuo et al (2012), the authors highlighted a few limitations of LEACH. Furthermore, they have presented extensions of the LEACH by considering residual energy, node density and distance parameters in their CH selection process.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks

Singh

Soni

2017

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Purpose This paper aims to discuss a comprehensive survey on fuzzy-based clustering techniques. The determination of an appropriate sensor node as a cluster head straightforwardly affects a network’s lifetime. Clustering often possesses some uncertainties in determining suitable sensor nodes as a cluster head. Owing to various variables, selection of a suitable node as a cluster head is a perplexing decision. Fuzzy logic is capable of handling uncertainties and improving decision-making processes even with insufficient information. Then, state-of-the-art research in the field of clustering techniques has been reviewed. Design/methodology/approach The literature is presented in a tabular form with merits and limitations of each technique. Furthermore, the various techniques are compared graphically and classified in a tabular form and the flowcharts of important algorithms are presented with pseudocodes. Findings This paper comprehends the importance and distinction of different fuzzy-based clustering methods which are further supportive in designing more efficient clustering protocols. Originality/value This paper fulfills the need of a review paper in the field of fuzzy-based clustering techniques because no other paper has reviewed all the fuzzy-based clustering techniques. Furthermore, none of them has presented literature in a tabular form or presented flowcharts with pseudocodes of important techniques.

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N-LEACH, a balanced cost cluster-heads selection algorithm for Wireless Sensor Network

Cited by 14 publications

References 12 publications

A Convergent Algorithm for Energy-Balanced Cluster-Heads Selection in Wireless Sensor Networks

A Convergent Algorithm for Energy-Balanced Cluster-Heads Selection in Wireless Sensor Networks

A Clustering Topology for Wireless Sensor Networks - New Semantics over Network Topology

A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks

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