Particle Swarm Optimization and Voronoi diagram for Wireless Sensor Networks coverage optimization

Aziz, Nor Azlina Bt Ab.; Mohemmed, Ammar; Sagar, B. S. Daya

doi:10.1109/icias.2007.4658528

Cited by 64 publications

(41 citation statements)

References 10 publications

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“…Objective of the centralized, off-line PSO-Voronoi algorithm proposed by Aziz et al in [13] is to minimize the area of coverage holes. The strategy is based on the principle that if each point in the region-of-interest (ROI) is covered by a sensor, then the whole ROI is covered.…”

Section: A Stationary Node Positioningmentioning

confidence: 99%

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

Kulkarni

Venayagamoorthy

2011

IEEE Trans. Syst., Man, Cybern. C

633

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 Stationary Node Positioningmentioning

confidence: 99%

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

Kulkarni

Venayagamoorthy

2011

IEEE Trans. Syst., Man, Cybern. C

633

244

View full text Add to dashboard Cite

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“…The algorithm named particle swarm genetic optimization (PSGO), which imported selection and mutation operators in the PSO to overcome the premature fault of classical PSO, was proposed to redeploy the mobile robots according to the node density for repairing the sensing coverage hole after their initial random deployment [9]. Aiming at the coverage problem of wireless sensor networks, Aziz et al proposed an algorithm to optimize sensor coverage using PSO and Voronoi diagram, in which PSO was used to find the optimal deployment of the sensors that gave the best coverage and Voronoi diagram was used to evaluate the fitness of the solution [10][11]. Zhiming et al provided a method of improved particle swarm optimization (IPSO) to solve the node deployment problem in wireless sensor networks which was always consist of stationary and mobile sensor nodes, and evaluated the coverage ratio achieved using the traditional VFA and IPSO [12].…”

Section: Related Workmentioning

confidence: 99%

Optimal Coverage Algorithm of Wireless Sensor Networks Based on Particle Swarm Optimization with Coherent Velocity

Wang

Sun²,

Feng³

2016

IJGDC

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“…The minimum number of sensor nodes was determined by using the following equation which was derived by [14,19]:…”

Section: Network Modelmentioning

confidence: 99%

“…Some of these techniques are individual particle optimization (IPO) [8], virtual force directed coevolutionary particle swarm optimization (VFCPSO) [9], improved particle swarm optimization (IPSO) [10], PSO with fuzzy logic [11] and intelligent single particle optimizer (ISPO) [12]. Aziz et al in papers [13,14] optimized the sensor node coverage using PSO for optimal placement and Voronoi diagram to evaluate the fitness of the solution. Other biologically inspired techniques that have been proposed are optimized artificial fish swarm algorithm (OAFSA) [15] and Optimization scheme of N-node (OPEN) that utilized genetic algorithm (GA) to obtain the near optimal solution [16].…”

Section: Introductionmentioning

confidence: 99%

Sensor Node Placement in Wireless Sensor Network Based on Territorial Predator Scent Marking Algorithm

Abidin

Din

2013

ISRN Sensor Networks

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Optimum sensor node placement in a monitored area is needed for cost-effective deployment. The positions of sensor nodes must be able to provide maximum coverage with longer lifetimes. This paper proposed a sensor node placement technique that utilizes a new biologically inspired optimization technique that imitates the behaviour of territorial predators in marking their territories with their odours, known as territorial predator scent marking algorithm (TPSMA). The TPSMA deployed in this paper uses the maximum coverage objective function. A performance study has been carried out by comparing the performance of the proposed technique with the minimax and lexicographic minimax (lexmin) sensor node placement schemes in terms of coverage ratio and uniformity. Uniformity is a performance metric that can be used to estimate a WSN lifetime. Simulation results show that the WSN deployed with the proposed sensor node placement scheme outperforms the other two schemes with larger coverage ratio and is expected to provide as long lifetime as possible.

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Particle Swarm Optimization and Voronoi diagram for Wireless Sensor Networks coverage optimization

Cited by 64 publications

References 10 publications

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

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

Optimal Coverage Algorithm of Wireless Sensor Networks Based on Particle Swarm Optimization with Coherent Velocity

Sensor Node Placement in Wireless Sensor Network Based on Territorial Predator Scent Marking Algorithm

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