Energy Efficient Cluster Formation in Wireless Sensor Networks Using Cuckoo Search

Dhivya, M.; Sundarambal, M.; Vincent, J. Oswald

doi:10.1007/978-3-642-27242-4_17

Cited by 13 publications

(4 citation statements)

References 6 publications

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“…In [93,94], the authors proposed a meta-heuristic approach called cuckoo search technique for forming the energy-efficient clusters. According to this algorithm, the SNs are segregated into two types such as low R e node and high R e node.…”

Section: Cluster Formation By Using Cuckoo Searchmentioning

confidence: 99%

Survey on fault tolerance‐based clustering evolution in WSN

Mohapatra

Rath

2020

IET Networks

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Ongoing research on remote communication and sensing essentially made accessible the flexible technology called 'wireless sensor network (WSN)', which is independent, self-organising and self-healing in nature. The WSN comprises an extensive number of homogeneous or heterogeneous sensor nodes that may be deployed in a uniform or non-uniform manner in a targeted zone. The organisation of WSN can be stretched out from residential to a harsh and hostile environment. The manageability of WSN is very subjective to an efficient adaptation to the failure situations. In this line of thought, clustering has been proven as an efficient strategy for prolonging the sensor network lifetime and selecting the correct topological structure for the sensor network. The authors' first present a survey of existing survey works from 2002 to 2019 and then present a tutorial on existing fault-tolerant protocols with a comparative analysis. The target audience of this work is novice researchers in the field of WSN to get a preliminary idea about clustering and its objective.

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Section: Cluster Formation By Using Cuckoo Searchmentioning

confidence: 99%

Survey on fault tolerance‐based clustering evolution in WSN

Mohapatra

Rath

2020

IET Networks

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“…This solver has been utilized for problems stemming from wireless networks, e.g. data aggregation [59], localization [60] or cluster formation [61]. Results have been also reported using other nature-inspired techniques farther from the literature mainstream, such as the so-called Social Emotional Optimization Algorithm [62], Intelligent Water Drops [63], Bat Algorithm [64], Bacterial Foraging [65] and Inmune Systems [66], among others.…”

Section: Related Workmentioning

confidence: 99%

Cost-efficient deployment of multi-hop wireless networks over disaster areas using multi-objective meta-heuristics

et al. 2018

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“…This section reviews the application of Cuckoo Search by previous researches. Based on the review papers, the major area or categories that used Cuckoo Search are Engineering [9,11,16,24,25], Pattern Recognition [1,8,19,20], Software Testing & Data Generation [2,3,12,17], Networking [14,15], Job Scheduling [4,18] and Data Fusion and Wireless Sensor Networks [6,7].…”

Section: Previous Studies On Application Of Cuckoo Searchmentioning

confidence: 99%

Cuckoo Search Algorithm for Optimization Problems - A Literature Review

Mohamad

Zain

Bazin

et al. 2013

AMM

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Cuckoo Search (CS) is an optimization algorithm developed by Yang and Deb in 2009. This paper describes an overview of CS which is inspired by the life of a bird family, called Cuckoo as well as overview of CS applications in various categories for solving optimization problems. Special lifestyle of Cuckoo and their characteristics in egg laying and breeding has been the basic motivation for this optimization algorithm. The categories that reviewed are Engineering, Pattern Recognition, Software Testing & Data Generation, Networking, Job Scheduling and Data Fusion and Wireless Sensor Networks. From the reviewed CS mostly applied in engineering area for solving optimization problems. The objective of this paper is to provide overview and summarize the review of application of the CS.

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Energy Efficient Cluster Formation in Wireless Sensor Networks Using Cuckoo Search

Cited by 13 publications

References 6 publications

Survey on fault tolerance‐based clustering evolution in WSN

Survey on fault tolerance‐based clustering evolution in WSN

Cost-efficient deployment of multi-hop wireless networks over disaster areas using multi-objective meta-heuristics

Cuckoo Search Algorithm for Optimization Problems - A Literature Review

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