A Density-Based Clustering Scheme for Wireless Sensor Networks

Kim, Jeong-Sam; Byun, Tae-Young

doi:10.1007/978-3-642-24267-0_32

Cited by 7 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In D-LEACH [9], each cluster is organized as in LEACH, i.e. there is some cluster heads (CHs) and cluster membership is determined in a distributed manner.…”

Section: Density-based Leachmentioning

confidence: 99%

See 1 more Smart Citation

A Centralized Cluster Head Selection Scheme for Reducing Discrepancy among Clusters over WSN

Jang

Byun

2012

Lecture Notes in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper, we present a cluster head selection scheme that considers residual energy of a node and local node density to maintain cluster size and the number of clusters, which dynamically adjusts cluster size to a recommended threshold with the ever changing network dynamics of sensor network. In previous representative clustering schemes such as LEACH and D-LEACH, cluster heads are selected with a recommended probability in a distributed manner. So, there are great deviations of the number of clusters and cluster size per cluster at every round during network lifetime. To improve these discrepancies in clusters, our proposed scheme selects cluster heads on basis of amount of residential energy of nodes in centralized manner. Finally, our cluster head selection scheme can reduce discrepancies of the cluster size and the number of clusters across network lifetime comparing with existing schemes, and can potentially prolong the network lifetime.

show abstract

“…In D-LEACH [9], each cluster is organized as in LEACH, i.e. there is some cluster heads (CHs) and cluster membership is determined in a distributed manner.…”

Section: Density-based Leachmentioning

confidence: 99%

“…Finally, BS selects 5% nodes with higher amount of residual energy as cluster heads for next round. The number of selected cluster heads can be calculated as follows [9,10]. ( 2 ) where, K is the number of cluster heads, N is the number of live nodes in current round, and P is percentage of cluster heads to all nodes.…”

Section: Key Ideasmentioning

confidence: 99%

A Centralized Cluster Head Selection Scheme for Reducing Discrepancy among Clusters over WSN

Jang

Byun

2012

Lecture Notes in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…But in this paper, we will focus on examples of density-based ones. In [2], the author proposed Density-based LEACH (D-LEACH). It uses the same stages of LEACH but the probability of any node to join a selected CH depends on the density around that CH.…”

Section: Related Workmentioning

confidence: 99%

“…He uses the benefit of the game theory to solve the problem of CHs selection. Given a network with area equal clusters, the transmission range of each calculated from equation (2). Then, the node is determined by equation (3) which is the nodes which has distance to less than or each node calculates a utility function as (4).…”

Section: Related Workmentioning

confidence: 99%

Energy efficient density-based clustering technique for wireless sensor network

Ellatief

Younes

Ahmed

et al. 2016

2016 8th International Conference on Knowledge and Smart Technology (KST)

View full text Add to dashboard Cite

Sensor nodes are characterized with limited resources of processing, memory, and battery. These features motivate the researchers to propose power-aware communication protocols. Clustering is used to help for this purpose. It is used to organize the massive number of deployed sensors in the network to minimize energy consumption. Different categories of clustering techniques were proposed. One of these categories is densitybased clustering which mainly depends on measuring the density around nodes before grouping them into clusters. This paper proposes an Energy-Efficient Density-based clustering technique which aims to balance the energy consumption among all clusters. This is done by the adaptation of the transmission range of cluster heads to use a suitable value according to the density around it. Simulation results for the proposed technique shows its effectiveness as it achieves less power consumption and more network lifetime when compared with other density-based clustering techniques.

show abstract

“…the number of clusters. In density-based clustering, regions of different densities are isolated and the sensor nodes lying in these regions are assigned in the same cluster [11,12]. However, this methodology cannot cluster data sets well with large differences in densities.…”

Section: Introductionmentioning

confidence: 99%