Topology-controlled adaptive clustering for uniformity and increased lifetime in wireless sensor networks

Dahnil, Dahlila Putri; Singh, Yashwant; Ho, Chin Kuan

doi:10.1049/iet-wss.2012.0034

Cited by 56 publications

(39 citation statements)

References 12 publications

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“…Ratio of residual energy to average energy of the network, ratio of initial energy to residual energy and distance to CM with respect distance sink are the different options for selecting CH in current round [1,8,10,11,[14][15][16]20,[23][24][25]31,35,39,42,44].…”

Section: ) Ratio Of Parametersmentioning

confidence: 99%

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A Review on Energy Efficient Clustering Protocol of Heterogeneous Wireless Sensor Network

Purkar¹,

R.S²

2017

IJET

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Section: ) Ratio Of Parametersmentioning

confidence: 99%

“…The time span after the start of network to death of last available node from the network, is termed as lifetime of WSN [1,6,7,10,15,16,19,[22][23][24][27][28][29]32,33,38,39,43,47].…”

Section: ) Lifetime Periodmentioning

confidence: 99%

A Review on Energy Efficient Clustering Protocol of Heterogeneous Wireless Sensor Network

Purkar¹,

R.S²

2017

IJET

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“…Existing ways for lifetime enhancement are classified into five categories: operation mode control [6], data processing [7][8], sink relocation [9]- [11], topology control [12] [13], and optimal routing [14]- [16]. There are various definitions of the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

Zhang¹,

Zhang

Gong³

et al. 2016

IEEE Trans. Evol. Computat.

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Kuhn-Munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks. IEEE Transactions on Evolutionary Computation.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/118977/ Abstract-Operating mode scheduling is crucial for the lifetime of wireless sensor networks. However, the growing scale of networks has made such a scheduling problem more and more challenging, as existing set cover and evolutionary algorithms become unable to provide satisfactory efficiency due to the curse of dimensionality. In this paper, a Kuhn-Munkres parallel genetic algorithm is developed to solve the set cover problem and is applied to lifetime maximization of large-scale wireless sensor networks. The proposed algorithm schedules the sensors into a number of disjoint complete cover sets and activates them in batch for energy conservation. It uses a divide-and-conquer strategy of dimensionality reduction, and the polynomial Kuhn-Munkres algorithm are hence adopted to splice the feasible solutions obtained in each subarea to enhance the search efficiency substantially. To further improve global efficiency, a redundant-trend sensor schedule strategy is developed. Additionally, we meliorate the evaluation function through penalizing incomplete cover sets, which speeds up convergence. Eight types of experiments are conducted on a distributed platform to test and inform the effectiveness of the proposed algorithm. The results show that it offers promising performance in terms of the convergence rate, solution quality, and success rate.Index Terms-parallel genetic algorithm, set cover problem, large-scale wireless sensor networks, Kuhn-Munkres algorithm.

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“…In TCAC [7], both residual energy and average energy of network are taken into consideration. CHs collect the residual energy of CMs and communicate with other CHs to obtain the total energy, then calculate the average energy and send it to all surrounding CMs.…”

Section: Introductionmentioning

confidence: 99%

“…Topology control is one of the most important technologies for energy saving in WSNs, which can generally be divided into two categories, the hierarchical topological structure [3,4] and node power control [5]. With the development of WSN technologies, combination of a variety of ways is a direction of topology control study [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Power Controlled Energy-efficient Clustering Scheme for Heterogeneous Wireless Sensor Networks

Qiao¹,

Zhao²,

Jun³

2017

dtcse

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Topology control is one of the most important technologies in wireless sensor networks. Many applications, combined with the methods of power control and hierarchical topology control strategy, can ultimately achieve the purpose of energy saving and topology optimization. Taken into consideration both power control and average energy of adjacent region, the paper proposes an energy efficient clustering algorithm for heterogeneous WSNs, called Power Controlled Clustering algorithm with Average Energy of Adjacent Region (PCCAEAR). In PCCAEAR, the cluster heads election process is divided into two stages. Firstly, the candidate cluster heads are elected by a probability based on the ratio between residual energy of each node and average energy of adjacent region. Secondly, a regional cluster head can be identified as the node with the highest residual energy within a certain range, while a candidate cluster head is communicating with other reachable candidate cluster heads in a certain transmission power. The cluster heads are elected in a stable number per round and evenly distributed in network, which make the network energy consumption more balanced. Simulation results show that PCCAEAR algorithm has a longer network lifetime, with the first node dead round number increasing by 6%, 14% and 22% respectively compared to DEEC, TCAC and REAC algorithms.

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Topology-controlled adaptive clustering for uniformity and increased lifetime in wireless sensor networks

Cited by 56 publications

References 12 publications

A Review on Energy Efficient Clustering Protocol of Heterogeneous Wireless Sensor Network

A Review on Energy Efficient Clustering Protocol of Heterogeneous Wireless Sensor Network

Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

A Power Controlled Energy-efficient Clustering Scheme for Heterogeneous Wireless Sensor Networks

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