Clustering algorithm for non‐uniformly distributed nodes in wireless sensor network

Tripathi, Rajiv K.; Singh, Yatindra Nath; Verma, Nishchal K.

doi:10.1049/el.2012.3512

Cited by 24 publications

(18 citation statements)

References 8 publications

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“…In this paper, the target weighting method is adopted by the proposed NPFPSO algorithm [23]; it can integrate hot ( ( )) and ordinary ( ( )) with linear fusion, so the problem can be solved by converting into a single objective function. The fitness function of NPFPSO can be calculated as the following formula: (23) where the weighting coefficient ∈ [0, 1] and it can be used to make a balanced control for the effective coverage of the hotspot region and ordinary region.…”

Section: Npfpso Algorithm For Nonuniform Coveragementioning

confidence: 99%

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Dynamic Deployment for Hybrid Sensor Networks Based on Potential Field-Directed Particle Swarm Optimization

Ying

Yunlong

Zhao

et al. 2015

International Journal of Distributed Sensor Networks

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For a hybrid sensor network, the effective coverage rate can be optimized by adjusting the location of the mobile nodes. For many deployments by APF (artificial potential field), due to the common problem of barrier effect, it is difficult for mobile nodes to diffuse by the weaker attraction when the nodes initially distribute densely in some places. The proposed deployment algorithm PFPSO (Potential Field-Directed Particle Swarm Optimization) can overcome this problem and guide the mobile nodes to the optimal positions. Normally the requirement is different for the effective coverage rate between the hotspot area and the ordinary area. On the basis of PFPSO, NPFPSO (Nonuniform PFPSO) algorithm was also proposed to implement nonuniform coverage according to the importance degree of the monitoring area. Simulation result illustrates that PFPSO algorithm can effectively improve the effective coverage rate of the network, and NPFPSO algorithm can obtain a balanced result of effective coverage rate for both hotspot area and ordinary area.

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Section: Npfpso Algorithm For Nonuniform Coveragementioning

confidence: 99%

“…(ii) The hotspot region is in the left bottom corner of target area. Figure 11 illustrates the coverage state, displacement of mobile nodes, and coverage curves when NPFPSO algorithm is performed for 50 iterations with weight parameter = 0.6 in formula (23).…”

Section: Npfpso Performance Analysismentioning

confidence: 99%

Dynamic Deployment for Hybrid Sensor Networks Based on Potential Field-Directed Particle Swarm Optimization

Ying

Yunlong

Zhao

et al. 2015

International Journal of Distributed Sensor Networks

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“…Tripathi et al [66] introduced clustering of non-uniform random distributed nodes, and calculated the optimal number of clusters in WSN. Results show that there is balanced energy expenditure in this nonuniform clustering.…”

Section: Probabilistic and Non-uniform Node Distribution 412mentioning

confidence: 99%

“…It is useful for homogeneous type of applications [64] It provide on an average a 28 % reduction in total energy usage over other existing algorithm It can not apply for location aware applications This scheme would be very suitable in applications where either the required accuracy in data is low or the data has very high redundancy [65] Its provide the perfect selection of optimal number of clusters This algorithm has less fault tolerance The application of this technique in WSNs is possible due to the redundancy in sensor readings and the large number of nodes deployed [66] Energy Efficient Complexity of the system increases It can be applied to practical scenario like a battlefield where we are placing nodes in an uncontrolled manner [67] High Network lifetime ………………………… It is applicable for non-uniform distributed nodes in WSNs…”

Section: It Is Not Applicable For Asymmetrical Channelmentioning

confidence: 99%

A Review Study on Analytical Estimation of Optimal Number of Clusters in Wireless Sensor Networks

Kumar

Dhok

Tripathi

et al. 2014

TNC

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To improve energy efficiency, total network scalability and data aggregation in Wireless Sensor Networks (WSNs), sensor nodes are often grouped into disjoint and mostly non-overlapping subsets called clusters. To provide an energy-efficient system by clustering, there are three main challenges. First is to find the optimum number of nodes in a specific cluster, second is to find the optimum number of clusters in the network and the third is to find the optimum position of Cluster Head (CH) in a specific cluster. Selecting an optimum number of clusters in WSNs provide greater improvement in terms of system scalability, energy efficiency, collision reduction, network lifetime, latency, and efficient routing backbone in the network. Selection of optimal number of clusters in WSNs is affected by level at which WSNs is modeled viz. Radio Energy Model Level, Network Model Level and Clustering Level. The objective of this paper is to present a state-of-the-art survey of distinct analytical methods used to calculate the optimum number of clusters, and its time-line comparative analysis based on network type, mathematical formula for an optimal number of clusters, base station positioning, energy model, strengths, weaknesses and applications of WSNs. We have also discussed the impact of different parameters on selecting the optimal number of clusters in WSNs.

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“…Tripathi et al [8] established an expression for non-uniformly deployed sensor nodes in the circular field with radius R. They consider that the sink is nearer to the nodes, so only free space loss is taken.…”

Section: Introductionmentioning

confidence: 99%

Optimum Number of Clusters in Randomly Distributed Wireless Sensor Networks

Chandrawanshi¹,

Tripathi²,

Pachauri³

2018

IJET

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The wireless sensor networks (WSN) are energy constraint because of finite batteries used as energy resources. The efficient utilization of these energy resources and prolonging lifetime are the most crucial research challenges in WSN. Clustering is considered one of the best suitable technique that carries out efficient resource allocation and higher scalability for sensor networks. The clustered networks have a big concern on the optimum number of clusters in a network. In a WSN, such a number of clusters significantly contribute in the betterment of network life time, energy resource utilization, system scalability and efficient data routing. In the proposed work, an analytical approach has been explored that provides optimum number of cluster in a WSN for various topologies. The proposed method takes both free space and multipath losses into consideration by assuming that the base station is located at the boundary of the sensing field.

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Clustering algorithm for non‐uniformly distributed nodes in wireless sensor network

Cited by 24 publications

References 8 publications

Dynamic Deployment for Hybrid Sensor Networks Based on Potential Field-Directed Particle Swarm Optimization

Dynamic Deployment for Hybrid Sensor Networks Based on Potential Field-Directed Particle Swarm Optimization

A Review Study on Analytical Estimation of Optimal Number of Clusters in Wireless Sensor Networks

Optimum Number of Clusters in Randomly Distributed Wireless Sensor Networks

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