Allocating Multiple Base Stations under General Power Consumption by the Particle Swarm Optimization

Hong, Tzung-Pei; Shiu, Guo-Neng

doi:10.1109/sis.2007.368022

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…Hong et al have PSO Multi-Base for optimal positioning of multiple base stations in a two tier WSN [17]. The two tier network consists of nodes that can communicate only with the application nodes they are assigned to.…”

Section: Base Station 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: Base Station 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

show abstract

“…The base station is usually assumed to have unlimited energy and powerful processing capability. In [ 47 ], a two-tiered wireless sensor network has been considered (see Figure 5 ) and an algorithm based on particle swarm optimization (PSO) has been proposed for finding the multiple base stations. The two tier network consists of nodes that can communicate only with the application nodes they are assigned to.…”

Section: Survey Of Existing Workmentioning

confidence: 99%

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

Adnan

Razzaque

Ahmed

et al. 2013

Sensors

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For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.

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“…Ease of implementation, high quality of solutions, computational efficiency, and speed of convergence are the advantages of the PSO. In literatures [5][6][7][8][9], PSO is used to discover the optimal WSN deployment. Static deployment is a one-time process in which solution quality is more important than fast convergence.…”

Section: Introductionmentioning

confidence: 99%

Linear Decision Fusion under the Control of Constrained PSO for WSNs

Jiang

Zhao

Mou

et al. 2012

International Journal of Distributed Sensor Networks

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A major application of a distributed WSN (wireless sensor network) is to monitor a specific area for detecting some events such as disasters and enemies. In order to achieve this objective, each sensor in the network is required to collect local observations which are probably corrupted by noise, make a local decision regarding the presence or absence of an event, and then send its local decision to a fusion center. After that, the fusion center makes the final decision depending on these local decisions and a decision fusion rule, so an efficient decision fusion rule is extremely critical. It is obvious that the decision-making capability of each node is different owing to the dissimilar signal noise ratios and some other factors, so it is easy to understand that a specific sensor contribution to the global decision should be constrained by this sensor decision-making capability, and, based on this idea, we establish a novel linear decision fusion model for WSNs. Moreover, the constrained particle swarm optimization (constrained PSO) algorithm is creatively employed to control the parameters of this model in this paper and we also apply the typical penalty function to solve the constrained PSO problem. The emulation results indicate that our design is capable of achieving very high accuracy.

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Allocating Multiple Base Stations under General Power Consumption by the Particle Swarm Optimization

Cited by 15 publications

References 18 publications

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

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

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

Linear Decision Fusion under the Control of Constrained PSO for WSNs

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