A particle swarm optimization approach for the localization of a wireless sensor network

Low, Kay Soon; Nguyen, Hong-Quang; Guo, Hao

doi:10.1109/isie.2008.4677205

Cited by 49 publications

(26 citation statements)

References 12 publications

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“…PSO-Beaconless: Low et al have proposed in [22] a PSO-based distributed localization scheme that does not involve beacons. The nodes are deployed by an unmanned aerial vehicle equipped with a position sensor.…”

Section: A Determination Of Locations Of Target Nodesmentioning

confidence: 99%

Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Kulkarni

Venayagamoorthy

2011

IEEE Trans. Syst., Man, Cybern. C

626

244

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Abstract-Wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bio-inspired techniques. Particle swarm optimization (PSO) is a simple, effective and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering and data-aggregation. This paper outlines issues in WSNs, introduces PSO and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues.

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Section: A Determination Of Locations Of Target Nodesmentioning

confidence: 99%

Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Kulkarni

Venayagamoorthy

2011

IEEE Trans. Syst., Man, Cybern. C

626

244

View full text Add to dashboard Cite

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“…Over the years, it has been successfully employed as an optimization tool in many fields due to its advantages of robustness, simplicity, and computational efficiency [47][48][49][50][51][52][53][54]. The PSO technique was originally adapted from the collective food searching behavior of animals, such as birds flocking and fish schooling.…”

Section: Theory Of Particle Swarm Optimizationmentioning

confidence: 99%

Optimized Droop Control Parameters for Effective Load Sharing and Voltage Regulation in DC Microgrids

Cingoz

Elrayyah

Sözer

2015

Electric Power Components and Systems

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“…The work is simulated and compared with the probabilistic method of selecting cluster heads and the experiments showed positive results. Further, the authors in [14] have used PSO to optimize the location of the sensors with an objective to enhance the network connectivity. Two types of optimization schemes are used, namely -single directional and bidirectional approaches.…”

Section: Related Workmentioning

confidence: 99%

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

Singh

Lobiyal

2012

Hum. Cent. Comput. Inf. Sci.

176

125

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The wireless sensor networks have long been an attractive field to the researchers and scientists for its ease in deployment and maintenance. In this research, we focus on the maximization of network lifetime which has become a critical issue in sensor networks. Clustered organization of nodes with aggregation of data at the cluster head becomes one of the significant means to extend life expectancy of the network. This paper proposes Particle Swarm Optimization (PSO) approach for generating energy-aware clusters by optimal selection of cluster heads. The PSO eventually reduces the cost of locating optimal position for the head nodes in a cluster. In addition, we have implemented the PSO-based approach within the cluster rather than base station, which makes it a semi-distributed method. The selection criteria of the objective function are based on the residual energy, intra-cluster distance, node degree and head count of the probable cluster heads. Furthermore, influence of the expected number of packet retransmissions along the estimated path towards the cluster head is also reflected in our proposed energy consumption model. The performance evaluation of our proposed technique is carried out with respect to the well-known cluster-based sensor network protocols, LEACH-C and PSO-C respectively. Finally, the simulation clarifies the effectiveness of our proposed work over its comparatives in terms of network lifetime, average packet transmissions, cluster head selection rounds supported by PSO and average energy consumption.

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A particle swarm optimization approach for the localization of a wireless sensor network

Cited by 49 publications

References 12 publications

Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Optimized Droop Control Parameters for Effective Load Sharing and Voltage Regulation in DC Microgrids

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

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