A routing algorithm for wireless sensor networks based on clustering and an fpt-approximation algorithm

Yarinezhad, Ramin; Hashemi, Seyed Naser

doi:10.1016/j.jss.2019.05.032

Cited by 41 publications

(21 citation statements)

References 38 publications

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“…The proposed algorithm effectively solves the problem of load balancing and constructs efficient inter-cluster routing, which improves the energy efficiency and prolongs the network lifetime. Like EB-CRP, CFPT [23] also aims to minimize the load of gateways, a f pt-approximation algorithm is used to assign network nodes to different gateways to form network clustering, but the approximation ratio of the f ptapproximation algorithm is 1.2. Simulation results show that the proposed clustering method can effectively balance the load of gateways.…”

Section: Related Workmentioning

confidence: 99%

“…However, the total distance between cluster heads is easily affected by the extreme values, leading to inaccurate judgment. Therefore, the index f 32 is designed, as shown in equation (23). f 32 is the sum of the distance from each node to the cluster head of its own cluster, which is used to measure the compactness within the cluster.…”

Section: Proposed Fitness Functionmentioning

confidence: 99%

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An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

et al. 2020

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Clustering sensor nodes is an effective method in designing routing algorithms for Wireless Sensor Networks (WSNs), which improves network lifetime and energy efficiency. In clustered WSNs, cluster heads are the key nodes, they need to perform more tasks, so they consume more energy. Therefore, it is an important problem to select the optimal cluster heads. In this paper, we propose a clustering algorithm that selects cluster heads using an improved artificial bee colony (ABC) algorithm. Based on the standard ABC algorithm, an efficient improved ABC algorithm is proposed, and then the network cluster head energy, cluster head density, cluster head location and other similar factors are introduced into the improved ABC algorithm theory to solve the clustering problem in WSNs. In the network initialization period, all nodes have the same energy level, the improved ABC algorithm is used to optimize fuzzy C-means clustering to find the optimal clustering method. We also propose an energy-efficient routing algorithm based on an improved ant colony optimization for routing between the cluster heads and the base station. In order to improve energy efficiency and further improve network throughput, in the stable transmission phase, we introduce a polling control mechanism based on busy/idle nodes into intra-cluster communication. The performance of the proposed protocol is evaluated in several different scenarios. The simulation results show that the proposed protocol has a better performance compared to a number of recent similar protocols. INDEX TERMS WSN, clustering, energy efficiency, network lifetime, high throughput, polling, routing algorithm, artificial bee colony

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Fitness Functionmentioning

confidence: 99%

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

et al. 2020

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“…In Sasikumar and Khara, two centralized and distributed k ‐means clustering algorithm is implemented as an alternative to the LEACH‐based protocols. From the perspective of energy efficiency mechanism, three distinct directions in the research works can be identified: sensor lifetime enhancement, load balancing, and network lifetime maximization. Yarinezhad and Hashemi proposed a virtual cellular infrastructure for traffic routing in WSN with mobile sink, which utilized the network structure information for sink information to save energy. In LBCH protocol, a novel method for initial cluster head selection is proposed to achieve clusters with even sizes.…”

Section: Related Workmentioning

confidence: 99%

A new rank‐order clustering algorithm for prolonging the lifetime of wireless sensor networks

Mostafavi

Hakami

2020

Int J Communication

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Energy efficient resource management is critical for prolonging the lifetime of wireless sensor networks (WSNs). Clustering of sensor nodes with the aim of distributing the traffic loads in the network is a proven approach for balanced energy consumption in WSN. The main body of literature in this topic can be classified as hierarchical and distance-based clustering techniques in which multi-hop, multi-level forwarding, and distance-based criteria are utilized for categorization of sensor nodes. In this study, we propose the approximate rank-order wireless sensor networks (ARO-WSNs) clustering algorithm as a combined hierarchical and distance-based clustering approach. Different from absolute distance, ARO-WSN algorithm utilizes a new rank-order distance measure for agglomerative hierarchical clustering. Specifically, for each sensor node, we generate a ranking order list by sorting all other sensor nodes in the neighborhood by absolute distance. Then, the rank-order distance of two sensor nodes is computed using their ranking orders. The designed algorithm iteratively group all sensor nodes into a small number of sub-clusters. The results show that ARO-WSN outperforms the competitive clustering algorithms in terms of efficiency and precision/recall. The lifetime of the network with the first node death criterion improved relative to LEACH, LEACH-C, LEACH with fuzzy descriptors, and BPA-CRP by 60%, 85%, 22%, and 18%, respectively, and with last node death criterion improved relative to K-means, LEACH, LEACH-C, and LEACH with fuzzy descriptors by 42%, 67%, 64%, and 24%, respectively. KEYWORDSapproximate rank-order, clustering, network lifetime, wireless sensor networks INTRODUCTIONWireless sensor networks (WSNs) have been utilized for a wide range of applications such as environmental monitoring, 1 target tracking, 2 health care monitoring, 3 and crisis management. 4 Sensors networks, as a group of collaborative sensors for a specific application, have made significant changes in the way that people interact with the environment. However, the WSNs face strict limitations in terms of communication bandwidth, battery, and computing power. These inherent constraints in sensor nodes have posed great challenges to the WSNs. Therefore, how to find cost-effective solutions to reduce energy consumption in WSN and efficiently exploit the scare resources in the network remains an important research area.Int J Commun Syst. 2020;33:e4313.wileyonlinelibrary.com/journal/dac

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“…The functioning of the sensor nodes with similar characteristics or in close proximity may seem necessary for energy efficient data collection in WSNs. This coordinated structure is called as clustering [1,2].…”

Section: Introductionmentioning

confidence: 99%

An Improved SEED Clustering Model for Wireless Sensor Networks

Yalçın

Erdem

2021

Politeknik Dergisi

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A routing algorithm for wireless sensor networks based on clustering and an fpt-approximation algorithm

Cited by 41 publications

References 38 publications

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

A new rank‐order clustering algorithm for prolonging the lifetime of wireless sensor networks

An Improved SEED Clustering Model for Wireless Sensor Networks

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