A hybrid clustering technique using quantitative and qualitative data for wireless sensor networks

Zhu, Jiang; Lung, Chung-Horng; Srivastava, Vineet

doi:10.1016/j.adhoc.2014.09.009

Cited by 39 publications

(19 citation statements)

References 25 publications

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“…Here, the hybrid technique combines two routing methods, namely, flat multihop routing and hierarchical multihop routing. The authors in [29] combined the ant colony optimization with greedy migration mechanism to improve the life time of sensor nodes under energy hole problem in grid based WSN. However, the hybrid techniques in [28][29] do not consider the clustering in WSN.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [29] combined the ant colony optimization with greedy migration mechanism to improve the life time of sensor nodes under energy hole problem in grid based WSN. However, the hybrid techniques in [28][29] do not consider the clustering in WSN. A hybrid clustering technique named Hybrid Distributed Hierarchical Agglomerative Clustering (H-DHAC) has been proposed in [30] that combines quantitative location and binary qualitative connectivity of data in clustering.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

Shankar

Shanmugavel²,

Rajesh

2016

Swarm and Evolutionary Computation

163

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

Shankar

Shanmugavel²,

Rajesh

2016

Swarm and Evolutionary Computation

163

View full text Add to dashboard Cite

“…This strategy helps to balance energy consumption in the network but the power control scheme is applied only to CHs. Zhu et al (2015) suggested to explicitly take account of the link quality. The resulting protocol named Hybrid Distributed Hierarchical Agglomerative (H-DHAC) and inspired by LEACH (Heinzelman et al, 2002) is based on two hierarchical clustering distance evaluation techniques commonly used in datamining and bioinformatics namely, Unweighted pair-Group Method with arithmetic Averages (UPGMA) and Weighted Pair Group Method with arithmetic Averages (WPGMA) (Romesburg, 2004).…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Ant Colony-Based k-Hop Clustering and Transmission Range Assignment Protocol for Connectivity Construction in Dense Wireless Sensor Networks

Diédié¹,

Aka²,

Babri³

2018

Journal of Computer Science

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Connectivity construction is the main phase of a communicationoriented topology control process. It consists of improving the current network physical topology while preserving important properties such as connectivity and symmetry. In this paper, we address the problem of combining two of the techniques commonly used for this purpose in networks composed of a large number of energy constrained wireless sensor-nodes namely, clustering and power control. We propose an ant colony-based asynchronous and localized protocol that helps to significantly reduce energy losses by simultaneously eliminating redundant and poor quality links, always keeping the Cluster Head-to-member distance up to khops (k≥1) and minimizing signalization. Simulation results show that our protocol outperforms some state-of-the-art solutions in terms of Quality of the Topology (QoT) and network lifetime prolongation.

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“…Usually this station is designed in a fixed local and with a long distance from the sensor nodes. In addition, the structure formed between the sensor nodes and cluster heads and base stations, can be repeated as many times as needed and create multiple layers in a hierarchy wireless sensor network [8][9][10].…”

Section: 2the Concept Of Clusteringmentioning

confidence: 99%

Increase the Lifetime of Wireless Sensor Networks Using Hierarchical Clustering with Cluster Topology Preservation

Baqeri¹,

Sedighimanesh²,

Sedighimanesh³

2016

IJWMN

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Wireless sensor networks consist of hundreds or thousands of nodes with limited energy. Since the life time of each sensor is equivalent to the battery life, the energy issue is considered as a major challenge. Clustering has been proposed as a strategy to extend the lifetime of wireless sensor networks. Cluster size, number of Cluster head per cluster and the selection of cluster head are considered as important factors in clustering. In this research by studying LEACH algorithm and optimized algorithms of this protocol and by evaluating the strengths and weaknesses, a new algorithm based on hierarchical clustering to increase the lifetime of the sensor network is proposed. In this study, with a special mechanism the environment of network is layered and the optimal number of cluster head in each layer is selected and then recruit for the formation of clusters in the same layer by controlling the topology of the clusters is done independently. Then the data is sent through the by cluster heads through the multi

show abstract

A hybrid clustering technique using quantitative and qualitative data for wireless sensor networks

Cited by 39 publications

References 25 publications

Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

Energy-Efficient Ant Colony-Based k-Hop Clustering and Transmission Range Assignment Protocol for Connectivity Construction in Dense Wireless Sensor Networks

Increase the Lifetime of Wireless Sensor Networks Using Hierarchical Clustering with Cluster Topology Preservation

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