Energy Efficiency Cluster Head Selection for Sensor Networks

Lee, Jong-Yong; Shrestha, Bhanu; Lee, Jong-Sup; Cho, Seongsoo

doi:10.14257/astl.2013.43.04

Cited by 6 publications

(7 citation statements)

References 5 publications

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“…According to the residual energy parameters of CHs and the number of members in the previous process, a reward-based clustering algorithm was proposed to periodically select CHs by learning methods [13]. Under the consideration of energy saving, a CH selection algorithm was proposed to select CHs by the density of member nodes and the distance from CHs, which is more energy efficient than only considering the distance between nodes [14]. Secondly, based on the CH selection process, a distributed channel selection algorithm was proposed, which balances the energy consumption between sensors based on the distance between sensors and base stations [15].…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Zhong

et al. 2021

Energies

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In order to realize the intelligent management of a power materials warehouse, the Internet of Things based on wireless sensor networks (WSNs) is a promising effective solution. Considering the limited battery capacity of sensor nodes, the optimization of the topology control and the determination of the amount of collected data are critical for prolonging the survival time of WSNs and increasing the satisfaction of the warehouse supplier. Therefore, in this paper, an optimization problem on sensor association and acquisition data satisfaction is proposed, and the subproblem of the sensor association is modeled as the knapsack problem. To cope with it, the block coordinate descent method is used to obtain the suboptimal solution. A sensor association scheme based on the ant colony algorithm (ACO) is proposed, and the upper and lower bounds of this optimization problem are also obtained. After this, a cluster head selection algorithm is given to find the optimal cluster head. Finally, the experimental simulations show that the algorithms proposed in this paper can effectively improve the energy utilization of WSNs to ensure the intelligent management of a power materials warehouse.

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Section: Literature Reviewmentioning

confidence: 99%

“…2: Initialize (λ, ν) 1 and r 1 , r 2 , r 3 = 0. Update M r 1 according to Equation (14) Update (λ, ν) r 1 +1 according to Equations ( 16) and (17), respectively.…”

Section: Algorithm 2 Data Collection and Topology Control Based On Aco (Dctc)mentioning

confidence: 99%

Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Zhong

et al. 2021

Energies

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“…Another distributed clustering algorithm is density and distance based CH selection algorithm (DDCHS) [21]. In the clustering step, the area is virtually divided into hexagons so that circular cluster overlap is prevented and in fact, some borders are considered for clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes

et al. 2019

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The Internet of things (IoT) provides the possibility of communication between smart devices and any object at any time. In this context, wireless nodes play an important role in reducing costs and simple use. Since these nodes are often used in less accessible locations, recharging their battery is hardly feasible and in some cases is practically impossible. Hence, energy conservation within each node is a challenging discussion. Clustering is an efficient solution to increase the lifetime of the network and reduce the energy consumption of the nodes. In this paper, a novel hybrid unequal multi-hop clustering based on density (HCD) is proposed to increase the network lifetime. In the proposed protocol, the cluster head (CH) selection is performed only by comparing the status of each node to its neighboring nodes. In this new technique, the parameters involving energy of nodes, the number of neighboring nodes, the distance to the base station (BS), and the layer where the node is placed in are considered in CH selection. So, in this new and simple technique considers energy consumption of the network and load balancing. Clustering is performed unequally so that cluster heads (CHs) close to BS have more energy for data relay. Also, a hybrid dynamic–static clustering was performed to decrease overhead. In the current protocol, a distributed clustering and multi-hop routing approach was applied between cluster members (CMs), to CHs, and CHs to BS. HCD is applied as a novel assistance to cluster heads (ACHs) mechanism, in a way that a CH accepts to use member nodes with suitable state to share traffic load. Furthermore, we performed simulation for two different scenarios. Simulation results showed the reliability of the proposed method as it was resulted in a significant increase in network stability and energy balance as well as network lifetime and efficiency.

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“…MECH algorithm [18] is another algorithm in which the clustering is performed on the basis of nodes' radio range and the number of nodes in each cluster. Another widely used algorithm is DDCHS [19]. In this algorithm, some virtual areas have been considered for clusters and for each hexagon a CH is selected.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Clustering Approach for Increasing the Lifetime of Wireless Sensor Networks Based on Bayesian Network

Orang¹,

Hajimohammadi²

2018

ujcmj

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Wireless sensor networks (WSNs) consist of a number of nodes and one or two base stations (BS). Each node has limited energy. Therefore, the energy each node is very important parameter in network since accessing the nodes and recharging them are difficult or in some cases, are impossible. Thus, the main purpose of this article is to increase the lifetime of the wireless sensor networks by finding the optimal route to send the data to the base station in order to save the energy of each node. In this paper, a hybrid clustering method called Hybrid based on Bayesian Networks (HBN) is proposed based on Bayesian network which considers the radio range of each nodes. In this algorithm, four different parameters are considered including residual energy, the distance to the base station, distance to the neighbor nodes and the radio range of the sensor nodes. According to the simulation results, this algorithm enables an increase in network lifetime in comparison to other similar algorithms.

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Energy Efficiency Cluster Head Selection for Sensor Networks

Cited by 6 publications

References 5 publications

Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes

A Hybrid Clustering Approach for Increasing the Lifetime of Wireless Sensor Networks Based on Bayesian Network

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