Layout optimization for a wireless sensor network using a multi-objective genetic algorithm

Jourdan, Damien; Weck, Olivier L. de

doi:10.1109/vetecs.2004.1391366

Cited by 156 publications

(115 citation statements)

References 9 publications

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“…Ağ alanı ve yoğunluğunun farklı değerleri için testler yapılarak sonuçlar alınmış ve eniyilenmiş baz istasyonu dinamikliğinin ağ yaşam süresini önemli ölçüde, baz istasyonunun hareketsiz olduğu duruma göre %90'lara varan seviyede arttırdığı gözlemlenmiştir. Jourdan ve arkadaşları [15] çalışmalarında düğüm yerleşimi için çok amaçlı genetik algoritma (MOGA) kullanılmıştır. Ağın kapsama alanını ve ağ ömrünü arttırmak için yapılan çalışmada KAA'ın düzensiz koşullarda (düzensiz arazi koşulları vb.)…”

Section: Yapay Zeka Algoritmaları Ve Programlama Modelleri üZerine Yaunclassified

A novel dynamic base station positioning method for wireless sensor networks

Tohma¹,

Kutlu²,

Abasıkeleş‐Turgut³

2017

Pamukkale J Eng Sci

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Section: Yapay Zeka Algoritmaları Ve Programlama Modelleri üZerine Yaunclassified

A novel dynamic base station positioning method for wireless sensor networks

Tohma¹,

Kutlu²,

Abasıkeleş‐Turgut³

2017

Pamukkale J Eng Sci

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“…Fig. 5 shows the convergence rate of PSO and GA. We ran the algorithm for both approaches several times and in every run PSO converges faster than GA, which was used in [1] for coverage and lifetime optimization. The main reason for the fast convergence of PSO is due to the velocity factor of the particle.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Most of the limitations are inherent to long-distance surveillance and cannot be resolved by any improvement in the onboard-sensor technology [1].…”

Section: Introductionmentioning

confidence: 99%

“…However they assumed that global information regarding other nodes is available. In [1], the authors examined the optimization of wireless sensor network layouts using a multi-objective genetic algorithm (GA) in which two competing objectives are considered, total sensor coverage and the lifetime of the network. However the computation of this method is not inexpensive.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we attempt to solve the coverage problem while considering energy efficiency using particle swarm optimization (PSO) algorithm, which can lead to computational faster convergence than genetic algorithm used to solve the deployment optimization problem in [1]. Sensor nodes are assumed to have mobility, and during the coverage optimization process, they move to form a uniformly distributed topology according to the execution of algorithm at the base station.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Swarm Based Sensor Deployment Optimization in Ad Hoc Sensor Networks

Shu

Yang

et al. 2005

Embedded Software and Systems

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Abstract. In ad hoc sensor networks, sensor nodes have very limited energy resources, thus energy consuming operations such as data collection, transmission and reception must be kept at a minimum. This paper applies particle swarm optimization (PSO) approach to optimize the coverage in ad hoc sensor networks deployment and to reduce cost by clustering method based on a wellknown energy model. Sensor nodes are assumed to be mobile, and during the coverage optimization process, they move to form a uniformly distributed topology according to the execution of algorithm at base station. The simulation results show that PSO algorithm has faster convergence rate than genetic algorithm based method while demonstrating good performance 1 .

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Energy-Efficient Deployment of Mobile Sensor Networks by PSO

Shu

Wang

et al. 2006

Lecture Notes in Computer Science

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Abstract. Sensor deployment is an important issue in designing sensor networks. In this paper, particle swarm optimization (PSO) approach is applied to maximize the coverage based on a probabilistic sensor model in mobile sensor networks and to reduce cost by finding the optimal positions for the clusterhead nodes based on a well-known energy model. During the coverage optimization process, sensors move to form a uniformly distributed topology according to the execution of algorithm at base station. The simulation results show that PSO algorithm has faster convergence rate than genetic algorithm based method while achieving the goal of energy efficient sensor deployment.

show abstract

Layout optimization for a wireless sensor network using a multi-objective genetic algorithm

Cited by 156 publications

References 9 publications

A novel dynamic base station positioning method for wireless sensor networks

A novel dynamic base station positioning method for wireless sensor networks

Swarm Based Sensor Deployment Optimization in Ad Hoc Sensor Networks

Energy-Efficient Deployment of Mobile Sensor Networks by PSO

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