DiCDU: distributed clustering with decreased uncovered nodes for WSNs

Abasıkeleş‐Turgut, İpek

doi:10.1049/iet-com.2019.0629

Cited by 10 publications

(6 citation statements)

References 26 publications

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“…The position information can be adopted to elect the optimal CH. Besides, the distribution of nodes follows the uniform distribution [25,31]. Besides, the processes of inter-cluster routing construction and data acquisition are beyond the scope of this paper.…”

Section: Related Assumptions and Notationsmentioning

confidence: 99%

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An energy‐efficiency‐adaptive clustering formation mechanism for the wireless sensor networks

et al. 2022

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Energy inequality caused by the process of cluster head election has a large influence on energy efficiency and the network lifetime of wireless sensor networks (WSNs). To this end, a novel concept of EIec is proposed to evaluate the equality degree of energy consumption. Related theorems for establishing the candidate set of cluster heads are proposed, with the aim of promoting energy equality in each cluster. Subsequently, a novel energy‐efficiency‐adaptive cluster formation mechanism based on economic (ECFE) theory is proposed and detailed. Finally, extensive experiments are carried out to assess its energy efficiency and the network performance by comparisons with the existing classic and latest intelligent clustering algorithms. The results indicate that ECFE improves not only the energy efficiency but also the network performance effectively.

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Section: Related Assumptions and Notationsmentioning

confidence: 99%

“…In fact, most of the problems in WSNs can be formulated into the multiobjective problem, with the aim of taking several factors concurrently [22,23]. Finally, some other clustering mechanisms which belongs to the cross-layer algorithm, such as those combined with the transport layer [24][25][26], the MAC layer [27,28] etc., were also proposed.…”

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confidence: 99%

An energy‐efficiency‐adaptive clustering formation mechanism for the wireless sensor networks

et al. 2022

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“…), and (g) network topology management (like scalability, etc.). While some previous studies support only one objective (eg, References 28,29), huge number of works support various objectives simultaneously (eg, References 25 and 30‐39).…”

Section: Related Workmentioning

confidence: 99%

“…Increasing the efficiency and scalability are the most important benefits of distributed algorithms. The ideas proposed in References 12,15,25,26,28,29,36,41,[54][55][56][57][58][59][60] are formed based on distributed clustering algorithm. Also, ideas proposed in References 13,14 are formed based on centralized clustering algorithm.…”

Section: Clustering Approachesmentioning

confidence: 99%

An energy‐aware and lightweight routing algorithm using clustering and mobile sinks in wireless sensor networks

Adabi

Mohammadifar

Nezhad

2022

Trans Emerging Tel Tech

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A wireless sensor network (WSN) consists of a number of small sensors which are powered by low-cost irreplaceable batteries. Therefore, one of the main issues in WSN design is to prolong the battery life of the individual sensors. Clustering and utilization of mobile sinks in collaboration with base station (BS) are two common solutions to address energy consumption challenge. Based on these solutions, a new algorithm in name "Energy-aware Routing algorithm using Clustering and Mobile Sinks (ERCMS)" is proposed in this article. The distinguishing features of ERCMS are as follows: (1) a geometric pattern is applied to divide entire network area into some parts and make cluster in each part. This pattern adjusts the cluster size based on the distance from the sink. Thus, hotspot problem is expected to be reduced, (2) computational load is distributed over sensors, (3) length of messages are controlled and each message is defined in the format with minimum required information, (4) simple mathematics approaches those impose lightweight computations are adopted, and (5) accuracy of mobile sink dispatching procedure is improved in critical conditions of the network. The simulation results show that ERCMS outperforms DFCR, MDFCR, and TEO-MCRP in terms of consumed energy, delivery ratio, delay, routing overhead, and throughput.

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“…In DiCDU, 23 unclustered nodes are incorporated into existing clusters to decrease the communication distance of unclustered nodes. Member nodes are encouraged to broadcast state messages to inform unclustered nodes about their clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

A fully distributed energy‐aware multi‐level clustering and routing for WSN‐based IoT

Abasıkeleş‐Turgut

Altan

2021

Trans Emerging Tel Tech

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One of the major problems in wireless sensor networks (WSNs) is that resource-constrained sensor nodes consume their limited batteries quickly due to long-distance data communications. The communication distance of the nodes can be decreased using clustering architectures and multi-hop data transmissions; hence, the lifetime of the network can be increased. In this study, two-level intra-cluster and multi-level inter-cluster communication are proposed. The coverage area of the second-level clusters is dynamically determined according to the distance of cluster heads to the BS. Self-organized nodes in the network designate the clustering ranges, the clusters, and the cluster heads without a central control mechanism using a fully distributed approach. Moreover, with the help of static clustering, the control messages generated by the system are decreased. The proposed approach, FDEAM, is compared with recent approaches in terms of the network lifetime, network energy consumption, cluster head alteration frequency, dead parent statistics, and data collection.The results show that FDEAM outperforms state-of-the-art approaches for all performance metrics.

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DiCDU: distributed clustering with decreased uncovered nodes for WSNs

Cited by 10 publications

References 26 publications

An energy‐efficiency‐adaptive clustering formation mechanism for the wireless sensor networks

An energy‐efficiency‐adaptive clustering formation mechanism for the wireless sensor networks

An energy‐aware and lightweight routing algorithm using clustering and mobile sinks in wireless sensor networks

A fully distributed energy‐aware multi‐level clustering and routing for WSN‐based IoT

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