Particle swarm optimization for the clustering of wireless sensors

Tillett, Jason C.; Rao, Raghuveer M.; Sahin, Ferat; Rao, T. M.

doi:10.1117/12.499080

Cited by 31 publications

(18 citation statements)

References 23 publications

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“…Particle swarm optimization 6 (PSO) has been applied to optimize the c-means model by Chen and Ye, 7 Cui et al, 8 and an extension to the fuzzy version was presented by Runkler and Katz. 9 Heuristic PSO clustering approaches for mobile ad hoc networks and wireless sensor networks were presented by Ji et al 10 and Tillett et al, 11 respectively. Xiao et al 12 combined a PSO approach with a self-organizing map and used this combination for clustering.…”

Section: Clusteringmentioning

confidence: 99%

Wasp swarm optimization of the c-means clustering model

Runkler

2008

Int. J. Intell. Syst.

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This paper deals with clustering by optimizing the c-means clustering model. For some data sets this clustering model possesses many local optima, so conventional alternating optimization (AO) will produce bad results. For obtaining good clustering results, the minimization procedure has to be kept from being trapped in these local optima, for example, by stochastic optimization approaches. Recently, we showed that ant colony optimization (ACO) can be effectively applied to the c-means clustering model. In this paper, we introduce a wasp swarm optimization (WSO) algorithm to optimize the c-means clustering model. In experiments with four benchmark data sets, the new WSO clustering algorithm is compared with AO and ACO. For data sets leading to c-means models without local optima, both WSO and AO perform better and faster than ACO. For data sets leading to multiple local optima, WSO clearly outperforms both AO and ACO. C

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Section: Clusteringmentioning

confidence: 99%

Wasp swarm optimization of the c-means clustering model

Runkler

2008

Int. J. Intell. Syst.

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“…The proposed algorithm synthesized the intuitionist advantages of graph theory [62] and optimal search capability of PSO [63]. They calculated the distance based on minimum spanning tree of the weighted graph of the WSN.…”

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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“…Most protocols are FH scheme. In [2], FC clustering method is proposed, which divides sensor nodes into different clusters in the way of recursive bisection using PSO (Particle Swarm Optimization) and elects cluster heads. Of course this clustering method is optimal, but the cost of cluster forming is high since it is dynamic clustering and the computation of PSO algorithm is NP-hard.…”

Section: Introductionmentioning

confidence: 99%

Energy Balanced and Efficient Clustering Method for Wireless Sensor Networks

Lee

Sin

Jang

et al. 2017

IJCT

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In this paper the energy balanced and efficient clustering method based on balance of energy consumption of nodes in WSN is proposed, which may be applied to any WSN. The almost static centralized protocol that differs from previous methods is proposed, the main feature of which is that the sinks transmit most of control message and process most of data. First, EBEC method is proposed, which optimizes by considering energy consumption on transmitting and receiving data, energy consumption on the reclustering and hot-spot problem that be optimized individually in previous works. In order to implement this method, VW BAK-C algorithm is used by introducing the concept of variable weighted Euclid distance to k-clustering algorithm. Second, the previous clustering methods are classified into random method and the method based on QoS according to the characteristic of cluster head rotation, and average of total energy consumption of nodes is analyzed mathematically. The proposed method is compared and analyzed. Third, the performance of the proposed method is evaluated by comparing with other clustering methods through simulation.

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Particle swarm optimization for the clustering of wireless sensors

Cited by 31 publications

References 23 publications

Wasp swarm optimization of the c-means clustering model

Wasp swarm optimization of the c-means clustering model

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

Energy Balanced and Efficient Clustering Method for Wireless Sensor Networks

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