Efficient Load-Balanced Clustering Algorithms for wireless sensor networks

Low, Chor Ping; Fang, Can; Ng, Jeffrey; Ang, Yew Hock

doi:10.1016/j.comcom.2007.10.020

Cited by 121 publications

(56 citation statements)

References 6 publications

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“…Therefore, energy conservation of the gateways is also very crucial for the long operation of the WSNs. Many energy-efficient clustering algorithms Low et al 2008; Bari et al 2008;Gupta and Younis 2003) have been proposed in this regard. However, none of the algorithms have considered routing load of CHs during cluster formation.…”

Section: Introductionmentioning

confidence: 99%

PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks

Azharuddin

Jana

2016

Soft Comput

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Many schemes have been proposed for energyefficient routing in wireless sensor networks (WSNs). However, most of these algorithms focus only on energy efficiency in which each node finds a shortest path to the base station (BS), but remain silent about energy balancing which is equally important to prolong the network lifetime. In this paper, we propose particle swarm optimization-based routing and clustering algorithms for WSNs. The routing algorithm builds a trade-off between energy efficiency and energy balancing, whereas the clustering algorithm takes care of the energy consumption of gateways as well as sensor nodes. We develop an efficient particle-encoding scheme and derive a multi-objective fitness function for each of the proposed routing and clustering algorithms. The algorithms are also capable of tolerating the failure of cluster heads. We perform extensive simulations on the proposed schemes and the results are compared with the existing algorithms to demonstrate their superiority in terms of various performance metrics.

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Section: Introductionmentioning

confidence: 99%

PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks

Azharuddin

Jana

2016

Soft Comput

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“…Rajan et al in their paper [22] have presented an overview of different bio-inspired population based metaheuristic algorithms for optimization problems in ad hoc networks. In [23], the authors have proposed a breadth-first search tree (BFS) based clustering algorithm for selection of minimal loaded CHs and affiliation of nodes to them. In [24], a Genetic Algorithm (GA) optimization technique has been developed which mimics the Darwinian theory of the survival of the fittest.…”

Section: Related Workmentioning

confidence: 99%

“…Many deterministic and stochastic algorithms have been developed in recent years for such networks [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. All the algorithms based on the gradient method are deterministic as they are hard computing based on a certain mathematical formula that cannot be changed.…”

Section: Related Workmentioning

confidence: 99%

Mobility Aware Energy Efficient Clustering for MANET: A Bio-Inspired Approach with Particle Swarm Optimization

Khatoon

Amritanjali

2017

Wireless Communications and Mobile Computing

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Mobility awareness and energy efficiency are two indispensable optimization problems in mobile ad hoc networks (MANETs) where nodes move unpredictably in any direction with restricted battery life, resulting in frequent change in topology. These constraints are widely studied to increase the lifetime of such networks. This paper focuses on the problems of mobility as well as energy efficiency to develop a clustering algorithm inspired by multiagent stochastic parallel search technique of particle swarm optimization. The election of cluster heads takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster heads for longer duration of time. The cluster formation is presented by taking multiobjective fitness function using particle swarm optimization. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The results show the effectiveness of our proposed algorithm in terms of network lifetime, average number of clusters formed, average number of reclustering required, energy consumption, and packet delivery ratio.

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“…Though, we focused on metaheuristic approach as our proposed algorithm is derived from it. In [26], Low et al have designed a clustering method by considering a BFS node tree to discover the slightest loaded head for allocating a node to a CH. The computational complexity of the method is O(mn 2 ) where n nodes with m cluster heads.…”

Section: Related Workmentioning

confidence: 99%

“…In this perspective, lots of researchers have recommended to bring into play of few extraordinary nodes termed as heads, which are furnished with added energy Copyright © 2017 MECS I.J. Intelligent Systems and Applications, 2017, 6, 66-74 [23,26,27,31]. These nodes acts like leaders of cluster and are accountable for the similar practicality of the CHs.…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization Approach for Energy Efficient Clustering in Wireless Sensor Networks

Singh¹,

Sharma²

2017

IJISA

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Abstract-In the previous years, wireless sensor networks (WSNs) got lot of attraction from the scientific and industrial society. WSNs are composed of huge number of small resource constrained devices recognized as sensors. Energy is a vital issue in WSN. Energy efficient clustering is an eminent optimization problem which has been studied extensively to prolong the lifetime of the network. This paper demonstrates the programming formulation of this problem followed by a proposed algorithm with particle swarm optimization (PSO) approach. The clustering method is stated by taking into consideration of energy saving of nodes. The proposed algorithm is experimented widely and results are evaluated with existing methods to show their supremacy in term of alive nodes, energy expenditure, packet delivery ratio, and throughput of network. Simulation results shows that our proposed algorithm outperform the other existing algorithms of its category.

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Efficient Load-Balanced Clustering Algorithms for wireless sensor networks

Cited by 121 publications

References 6 publications

PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks

PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks

Mobility Aware Energy Efficient Clustering for MANET: A Bio-Inspired Approach with Particle Swarm Optimization

A Particle Swarm Optimization Approach for Energy Efficient Clustering in Wireless Sensor Networks

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