Coverage in wireless sensor networks based on individual particle optimization

Salehizadeh, S. M. A.; Dirafzoon, Alireza; Menhaj, M B; Afshar, Ahmad

doi:10.1109/icnsc.2010.5461607

Cited by 19 publications

(13 citation statements)

References 25 publications

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“…Although VF based method ensures full coverage and full connectivity, Ingle and Bawane [7] claimed that this method relies highly on the sensor mobility that consume high energy. Furthermore, Salehizadeh et al [9] believed that VF performs well for WSN that are equipped with mobile sensor nodes only.…”

Section: Related Workmentioning

confidence: 99%

“…Most researchers deployed Particle Swarm Optimization (PSO) based algorithms for sensor node placement such as Virtual Force Directed Co-Evolutionary Particle Swarm Optimization (VFCPSO) algorithm [10], Improved Particle Swarm Optimization (IPSO) [11], Individual Particle Optimization (IPO) [9] while Aziz et al in papers [12] and [13] optimized the sensor coverage using PSO for optimal placement and Voronoi diagram to evaluate the fitness of the solution. The issue concerns in applying these techniques are the accuracy and convergence time.…”

Section: Related Workmentioning

confidence: 99%

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Sensor node placement based on minimax for effective surveillance

Abidin

Din

2012

2012 IEEE Symposium on Industrial Electronics and Applications

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Abilities in sensing, storing data, processing data and transmitting data has made Wireless Sensor Networks (WSN) to be applied in providing effective surveillance. Optimum sensor nodes placement in the surveillance area is needed since the number of sensor nodes that can be deployed are limited. The positions of sensor nodes must be able to provide maximum coverage. This paper describes an initial progress of an ongoing project. A sensor nodes placement technique which is based on minimax algorithm is proposed where surveillance locations will be monitored by the closest sensor nodes. Performance study has been carried out by comparing the performance of minimax and ideal value in terms of coverage ratio. Coverage ratio indicates the number of surveillance locations that received effective coverage. Simulation results obtained shows that coverage ratio produced by minimax technique is directly proportional to the number of sensor nodes and can be considered acceptable.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sensor node placement based on minimax for effective surveillance

Abidin

Din

2012

2012 IEEE Symposium on Industrial Electronics and Applications

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“…Xue et al proposed a virtual force directed co-evolutionary particle swarm optimization (VFCPSO) algorithm with the collaboration of multiple sensor nodes, in which a combined objective function was used to achieve the tradeoff of coverage and energy consumption [15]. Salehizadeh et al proposed a node deployment algorithm for mobile sensor networks based on individual particle optimization (IPO) for the purpose of maximum coverage in environment, and the mobile nodes would relocate themselves to find the best deployment under various kinds of situations in order to cover the largest area [16]. Aziz et al took the energy consumption into account, and gave a two phase PSO algorithm towards coverage maximization and energy conservation for mobile wireless sensor networks.…”

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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“…Although the VF-based method ensures full coverage and full connectivity, Ingle and Bawane [3] claimed that this method relies highly on the sensor node mobility that consumes high energy. Salehizadeh et al [6] also believed that VF performs well for mobile sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Suppose the coordinates of the node are ( , ), then the probability of node detects an object in ( , ) is [6]:…”

Section: Binary Sensing Modelmentioning

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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Coverage in wireless sensor networks based on individual particle optimization

Cited by 19 publications

References 25 publications

Sensor node placement based on minimax for effective surveillance

Sensor node placement based on minimax for effective surveillance

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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