Cluster-head identification in ad hoc sensor networks using particle swarm optimization

Tillett, Jason C.; Rao, Raghuveer M.; Sahin, Ferat

doi:10.1109/icpwc.2002.1177277

Cited by 83 publications

(52 citation statements)

References 4 publications

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“…They use a pre-determined optimal number of clusters (5% of the total number of nodes) in their simulations. Tillett et al [16] The New Genetic Based Method with Optimum Number of Super Node for Fault Tolerant System in Heterogeneous Wireless Sensor Network…”

Section: Related Workmentioning

confidence: 99%

The New Genetic Based Method with Optimum Number of Super Node for Fault Tolerant System in Heterogeneous Wireless Sensor Network

Navimipour¹,

Rahmani²

2010

IJCEE

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Abstract-In this paper the new method for determining the number of super node in heterogeneous wireless sensor network based on evolutionary algorithms are presented. The network consisting of several resource-rich supernodes, used for data relaying, and a large number of energy-constrained wireless sensor nodes. The main contribution of this paper is to reach an optimum trade off between number of super node and efficiency. Simulation results show that our algorithm can quickly find a good solution.

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Section: Related Workmentioning

confidence: 99%

The New Genetic Based Method with Optimum Number of Super Node for Fault Tolerant System in Heterogeneous Wireless Sensor Network

Navimipour¹,

Rahmani²

2010

IJCEE

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“…In the paper [7] by Tillett et al, the PSO (Particle Swarm Optimization) approach is proposed which would divide the sensor node field into groups of equal sized groups of nodes. PSO is an evolutionary programming technique that mimics the interaction of ants or termites to find a good solution.…”

Section: Related Workmentioning

confidence: 99%

Particle Swarm Optimization for Gene cluster Identification

Manasi¹,

Kamlapur²

2014

IJCATR

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Abstract-In this paper, we explored the possibility of using Genetic Algorithm (GA) being used in Wireless Sensor Networks in general with specific emphasize on Fault tolerance. In Wireless sensor networks, usually sensor and sink nodes are separated by long communication distance and hence to optimize the energy, we are using clustering approach. Here we are employing improved K-means clustering algorithm to form the cluster and GA to find optimal use of sensor nodes and recover from fault as quickly as possible so that target detection won't be disrupted. This technique is simulated using Matlab software to check energy consumption and lifetime of the network. Based on the simulation results, we concluded that this model shows significant improvement in energy consumption rate and network lifetime than other method such as Traditional clustering or Simulated Annealing.

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“…There have been some researches on clustering in wireless sensor networks as discussed in [1], [2], [3], [6], [7]. In [1] the cluster heads are identified once during network deployment by a central controller.…”

Section: Related Workmentioning

confidence: 99%

“…In [1] the cluster heads are identified once during network deployment by a central controller. Also in [2] the clusters are formed during the actual physical deployment of the sensor networks which have to be planned by the network designers in advance.…”

Section: Related Workmentioning

confidence: 99%

CAC: Context Adaptive Clustering for Efficient Data Aggregation in Wireless Sensor Networks

Jin

Park

2006

NETWORKING 2006. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile A

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Abstract. Wireless sensor networks are characterized by the widely distributed sensor nodes which transmit sensed data to the base station cooperatively. However, due to the spatial correlation between sensor observations, it is not necessary for every node to transmit its data. There are already some papers on how to do clustering and data aggregation in-network, however, no one considers about the data distribution with respect to the environment. In this paper a context adaptive clustering mechanism is proposed, which tries to form clusters of sensors with similar output data within the bound of a given tolerance parameter. With similar data inside a cluster, it is possible for the cluster header to use a simple technique for data aggregation without introducing large errors, thus can reduce energy consumption and prolong the sensor lifetime. The algorithm proposed is very simple, transparent, localized and does not need any central authority to monitor or supervise it.

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Cluster-head identification in ad hoc sensor networks using particle swarm optimization

Cited by 83 publications

References 4 publications

The New Genetic Based Method with Optimum Number of Super Node for Fault Tolerant System in Heterogeneous Wireless Sensor Network

The New Genetic Based Method with Optimum Number of Super Node for Fault Tolerant System in Heterogeneous Wireless Sensor Network

Particle Swarm Optimization for Gene cluster Identification

CAC: Context Adaptive Clustering for Efficient Data Aggregation in Wireless Sensor Networks

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