Energy-Efficient Routing in WSN: A Centralized Cluster-Based Approach via Grey Wolf Optimizer

Daneshvar, S. M. Mahdi H.; Mohajer, Pardis Alikhah Ahari; Mazinani, Sayyed Majid

doi:10.1109/access.2019.2955993

Cited by 125 publications

(120 citation statements)

References 27 publications

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“…In case if this distance is less than its threshold value (d th ), then it employs the free space model as a channel model or else the multipath fading model is chosen as a channel model. The energy utilization of the data based on the distance factor is mathematically given as follows [ 55 ]:

where E energy is a total dissipated energy of the circuit per bit, E tm and E am are the transmitter and amplifier model of the network, and d th is the threshold distance of the network and it is given as below in (8)

…”

Section: Proposed Methodsmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

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Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

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…”

Section: Proposed Methodsmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

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“…However, the single-hop routing mechanism will aggravate the fast energy consumption of nodes far away from the base station, which brings some limitations to the protocol. In [17], a clustering routing protocol based on Grey Wolf Optimizer (GWO) is proposed, it makes use of the infinite computing power and infinite energy of the base station to accurately calculate the energy that the network will consume in the next round, and the corresponding fitness function is proposed. The GWO is used to find the optimal solution of the problem, so as to select the optimal cluster head set.…”

Section: Related Workmentioning

confidence: 99%

“…After calculating the energy Gini coefficients of all clusters through equation 16, calculate the standard deviation of this group of energy Gini coefficients according to equation (17).…”

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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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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“…Simulation with Matlab / Simulink software and comparison with other optimization techniques show that the controller based on GWO has advantages in stability, speed and accuracy in applications such as robot power supply. In [36], the author researched routing protocols and proposed a new clustering algorithm to solve the problem of energy consumption in WSN. In order to improve energy efficiency, GWO is used to select cluster head nodes, and the protocol algorithm is allowed to use the same cluster in multiple consecutive rounds.…”

Section: Related Workmentioning

confidence: 99%

“…The Grey Wolf Optimizer (GWO), a swarm intelligence optimization algorithm that mimics the predation process of grey wolves, is a heuristic search algorithm proposed by Mirjalili et al [31]. Because the algorithm has good convergence ability and high optimization precision, it has been widely used in neural network training [32], [33], controller design [34], [35], wireless sensor network coverage [36], [37] and other research fields [38]- [40]. The optimization performance of the GWO algorithm, however, still has room for improvement.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

Wang

Xie

2020

IEEE Access

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Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is that the grey wolf population is divided into two parts, one part is responsible for the outer-layer encircle and the other is responsible for the inner-layer encircle, and the introduction of Tent mapping. The purpose of this is to enhance the exploitation and exploration ability of the algorithm respectively, so as to improve the convergence and optimization precision of the algorithm. In addition, in terms of WSN deployment in 3D surfaces, this paper improves the means of determining the perceived blind zone. Meanwhile, a novel method to calculate the WSNs coverage area of simple and complex 3D surfaces is presented by combining the grid and integral of the 3D surfaces. The EGWO is favorably compared with the GWO and three existing variants of the grey wolf optimizer when testing on 12 well-known benchmark functions. The simulation experiment results show that compared with the existing algorithms, EGWO can provide a very competitive search result in terms of optimization precision and convergence performance. Finally, this paper applies EGWO to the 3D surface deployment of WSN. Simulations show that compared with the other three deployment algorithms, EGWO can improve the network coverage of WSN, which can save network deployment costs. In addition, the probability of network connectivity deployed by EGWO is higher, that is, EGWO can provide a better deployment solution. INDEX TERMS Wireless sensor network, three-dimensional surface deployment, perceived blind zone, grid and integration, optimization, grey wolf optimizer, inner-layer encircle.

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Energy-Efficient Routing in WSN: A Centralized Cluster-Based Approach via Grey Wolf Optimizer

Cited by 125 publications

References 27 publications

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

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

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

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