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

Kumar, Vinay; Dhok, Sanjay B.; Tripathi, Rajeev; Tiwari, Sudarshan

doi:10.14738/tnc.25.488

Cited by 18 publications

(8 citation statements)

References 67 publications

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“…Forming the right number of chains (K) is an important step in hierarchal routing protocols since it manages less energy consumption. The optimal K value is affected by many factors, including the used energy model, the nodes' density, the BS location, the size of the sensing field and other factors [25]. In our protocol, we adopted an analytical model that is applicable to our network and energy models that were derived by N. Amini et al in [24].…”

Section: Decision About the Number Of Chainsmentioning

confidence: 99%

Determination of the K-optimal number of chains-based routing protocol formed by the K-Means algorithm for the WSN

Alnajdi¹,

Bajaber²

2020

IJEECS

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<span>Wireless sensor networks comprise of a large number of lightweight and relatively low-cost computational nodes which their main task is to sense the surrounding environment and collect the information to send it wirelessly to a central point to take the appropriate actions. Although these networks had been used in various applications, achieving this task is challenging due to the many constraints of sensor nodes including their limited processing power, communication bandwidth, and power supply. Therefore, an energy efficient routing protocols had to be developed specifically for sensor networks to insure longer lifetime and reasonable performance of the network. In this work, we propose an energy efficient hierarchical routing protocol using chain-based clustering. <span>By simulation on MATLAB, the proposed protocol proved to enhance the performance as it prolongs the lifetime of the network and decreases the energy consumption, the transmission delay, and the overhead compared to other existing protocols as it depends on some advanced methods including dynamic selection of number of chains method, k-means clustering method, advanced greedy chain construction method, and multi-factor based leader selection method.</span></span>

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Section: Decision About the Number Of Chainsmentioning

confidence: 99%

Determination of the K-optimal number of chains-based routing protocol formed by the K-Means algorithm for the WSN

Alnajdi¹,

Bajaber²

2020

IJEECS

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“…We assume that the cluster number is M such that (1 ⩽ M ⩽ N), as proposed in the study of Kumar et al 39 There are distinct analytical methods used to calculate the optimum number of clusters based on several parameters: node density, size of sensing fields, and BS positioning (center, inside, and outside). We use MILP optimization to minimize the amount of energy used to transmit data from ordinary nodes to the CH.…”

Section: Milp Formulation Of Eachsmentioning

confidence: 99%

Optimal load balanced clustering in homogeneous wireless sensor networks

Souissi

Meddeb

2016

Int J Communication

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Summary Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.

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“…In this work, network level parameters as base station positioning, node density and size of sensing fields have been addressed. [3] 2. Related Work A Low Energy Adaptive Clustering Hierarchy (LEACH) protocol has been proposed by the Heinzelman et al [4].…”

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

Cited by 18 publications

References 67 publications

Determination of the K-optimal number of chains-based routing protocol formed by the K-Means algorithm for the WSN

Determination of the K-optimal number of chains-based routing protocol formed by the K-Means algorithm for the WSN

Optimal load balanced clustering in homogeneous wireless sensor networks

Optimum Number of Clusters in Randomly Distributed Wireless Sensor Networks

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