Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks

Hussain, Sajid; Matin, A.W.; Islam, Obidul

doi:10.1109/itng.2007.97

Cited by 126 publications

(61 citation statements)

References 7 publications

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“…Depending on cluster formation criteria and parameters used for CH election the above clustering algorithms can be further divided into sub-categories as probabilistic and non-probabilistic clustering algorithms. Low Energy Adaptive Clustering Hierarchy Algorithm (LEACH) [4], Power Efficient Gathering In Sensor Information System (PEGASIS) [5], Power Efficient Data Gathering and Aggregation Protocol( PEDAP) [6] falls under category of probabilistic clustering algorithm while Highest Connectivity Cluster Algorithm (HCC) [7], Weight Based Clustering Algorithm(WBC) [8] , Genetic Algorithm [9] and Social insect colony falls under nonprobabilistic algorithms.…”

Section: Clustering and Computational Intelligence Techniques Amentioning

confidence: 99%

Lifetime Enhancement of Wireless Sensor Network by Efficient Clustering Using Nature Inspired Algorithm

Thakur¹

2018

IJRASET

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Wireless Sensor Network (WSN) is one of the most promising and leading technology which comprises of autonomous, multifunctional, self-governing battery powered devices known as sensor nodes. These sensor nodes are intelligent enough to collect information from the sensing environment. But there are various challenges in WSN and limited energy is one of them. Nature inspired algorithms provides best solutions to various challenges in WSN. This paper proposes a decentralized clustering algorithm for WSN based on the colonial behavior of social insects i.e. honey bee. In this paper we evaluate the clustering algorithm by using first order radio model. The purpose of this research paper is to form efficient clusters by optimal selection of cluster head on the basis of parameters such as distance, centrality, node degree and energy so that minimum energy for the transmission of data to the base station is consumed. Simulation results show that the proposed algorithm reduces the energy consumption and our network survives for a longer period of time.

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Section: Clustering and Computational Intelligence Techniques Amentioning

confidence: 99%

Lifetime Enhancement of Wireless Sensor Network by Efficient Clustering Using Nature Inspired Algorithm

Thakur¹

2018

IJRASET

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“…A Genetic Algorithm (GA) based approach is proposed in [4] for CH selection. A Particle Swarm Optimization (PSO) [5] algorithm is utilized to choose an optimal set of CH in each cycle.…”

Section: Related Workmentioning

confidence: 99%

Threshold-based Energy Efficient Clustering Protocol for Corrosion Risk Monitoring of Reinforced Concrete

Senniappan¹,

Subramanian²

2017

IJCA

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In recent years, wireless sensor networks (WSN) are being used by a wide range of applications. Sensor nodes are used for gathering data about the various physical parameters of the system under study. As sensor nodes are battery powered, energy efficiency is an important challenge faced by WSN. Clustering is a technique for improving the energy efficiency of the sensor network. The reported piece of work uses the soft computing technique, Biogeography-Based Krill Herd Algorithm (BBKH) for energy efficient clustering that results in improving the network lifetime. While cluster heads are selected, the rate adjustment is made for congestion mitigation. Likewise, a threshold based data forwarding method is also employed for further improvement of the network lifetime. Based on threshold principle, sending of similarly sensed data repeatedly is avoided. The network lifetime is compared before and later on using the threshold value. The obtained results after using the threshold concept are 18.80 % better than its threshold free counterpart. The proposed method is used in structural health monitoring (SHM) application where the corrosion risk is monitored in the reinforced concrete structures.

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“…Central control algorithm is used to form the clusters, may produce better cluster design by assigning the CH nodes throughout the network. Hussain et al (2007) introduced the Hierarchical Cluster based Routing (HCR) protocol (Zahmatkesh and Yaghmaee, 2012;Matin and Hussain, 2006). All the nodes are self-organized in to clusters and each cluster is managed by a set of associates called head-set and CH selected by Round Robin technique.…”

Section: Related Workmentioning

confidence: 99%

“…All the nodes are self-organized in to clusters and each cluster is managed by a set of associates called head-set and CH selected by Round Robin technique. Hussain et al (2007) improved the HCR protocol by using a heuristic-based approach. It uses GA to determine the cluster, CH and CH members and the transmission schedule (Hussain et al, 2007).…”

Section: Related Workmentioning

confidence: 99%

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Optimal Energy Consumption to Extend the Lifetime of Wireless Sensor Networks

Thangadurai¹

2013

Journal of Computer Science

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Recent advances in Wireless Sensor Networks (WSNs) enable us to develop minimum energy consumption clustering algorithms. The WSNs can be used for various applications like battle field, disaster areas and military applications. The most important issue in WSNs is the limited battery of sensor nodes. When sensor nodes are placed in a field, it is difficult to replace their batteries. To minimize the energy consumption of nodes, WSNs introduce various protocols and algorithms. Among the techniques, the genetic based energy efficient clustering algorithms are used to minimize the energy consumption of nodes and to extend the network lifetime. Genetic Algorithm (GA) is an adaptive method for search and optimize problems and the genetic process is based on biological organisms. Clustering is an effective way for reducing energy consumption of a sensor nodes as well as the cost of transmission. The proposed genetic algorithm based clustering protocol provide better efficiency in terms of delay, energy consumption. Here we simulated the proposed protocol by using network simulator ns2.34 and compared with other existing protocols.

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Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks

Cited by 126 publications

References 7 publications

Lifetime Enhancement of Wireless Sensor Network by Efficient Clustering Using Nature Inspired Algorithm

Lifetime Enhancement of Wireless Sensor Network by Efficient Clustering Using Nature Inspired Algorithm

Threshold-based Energy Efficient Clustering Protocol for Corrosion Risk Monitoring of Reinforced Concrete

Optimal Energy Consumption to Extend the Lifetime of Wireless Sensor Networks

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