A Grey Wolf Optimization Approach for Improving the Performance of Wireless Sensor Networks

Kaushik, Ajay; Indu, S.; Gupta, Daya

doi:10.1007/s11277-019-06223-2

Cited by 44 publications

(22 citation statements)

References 15 publications

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“…Grey wolf optimizer, a newly swarm intelligence algorithm introduced by Mirjalili et al [51], is a powerful meta-heuristic algorithm, which has the ability to compete with other algorithms including PSO, GA, DE and many other algorithms in terms of solution accuracy, minimum computational effort, and aversion of premature convergence [69,70]. Because of these advantages, it has been gained a very big research interest by tremendous audiences from several domains and successfully applied in the fields of global optimization [71], control engineering [72,73], feature selection [74], scheduling problems [75,76] in recent years.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

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Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

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Section: Grey Wolf Optimizermentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

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“…In [17], using a grey wolf optimization algorithm (DBSCDS-GWO) algorithm to achieve a stable, balanced, and efficient WSN, a distance-based dominant set-based set method is proposed. A stable distance-based clustering algorithm using GWO (DBSC-GWO) is also proposed to improve the performance of cluster WSN.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Optimization of QoS in Wireless Sensor Networks Using Multiobjective Grey Wolf Optimization Algorithm

Nabavi

Eraghi

Torkestani

2022

Wireless Communications and Mobile Computing

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With the advancement of technology and the emergence of new types of communication networks, new solutions have emerged to protect the environment and monitor natural resources. Wireless sensor networks (WSNs) have revolutionized environmental science and research by embedding sensors in environments where constant access and monitoring by manpower is difficult. WSNs have a variety of uses in the military, environmental monitoring, medicine, robotics, and so on. With the advent of applications in WSNs, the fundamental problem of the network has also increased. The performance of WSNs is influenced by various parameters that are varied according to the applications. In general, the performance of a WSN is typically specified through its average energy use, which determines the lifespan of the grid. A WSN should acquire the ability for controlling the overall performance of the network for ensuring the transmission of information based on quality of service (QoS) parameters in order to maximize the satisfaction of the services for the application. Therefore, we provided a multiobjective grey wolf optimization algorithm (QAMO-GWO) in order to optimize routing and improve QoS in WSNs. In the proposed method, sensor nodes receive information about the environment over regular periods of time and send it to the cluster heads in each. The selection of cluster heads in each cluster is done using a multiobjective grey wolf optimization algorithm. MO-GWO algorithm via balancing QoS parameters tries for selecting the optimal cluster heads. Finally, simulation outputs showed that the proposed method has been able to improve QoS criteria due to balancing the goals in the network.

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“…FaouziHidowssi et al (2017) [13] proposed PEAL (Power Efficient and Adaptive Latency) wireless networks, to extend network life time and achieved 47% more life time than the LEACH (Low Energy Adaptive Clustering Hierarchy) model but with more delay. Kaushik et al proposed a distance-based energy efficient and load balancing method for addressing the problems associated with compactly populated network such as interference, many transmission routes, to reduce the burden of distant nodes involved in communication for saving energy [22]. Reeta Bhardwaj et al [12] presented a Multi-objective fragmentary molecule lion calculation (MOFPL) to find a suitable team leader in various WSN locations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wireless sensor networks (WSNs) are evolving to provide affordable and adaptable solutions in environmental monitoring, habitat monitoring, home automation and smart grids [1] [2]. More number of sensor nodes are required in medical, military surveillance, weather forecasting, industrial and commercial applications Kaushik et al to guarantee acceptable coverage and to reduce node failure [22]. Nevertheless, a closely populated WSN cause interference between nodes, several transmission routes, consumption of more energy by each node during their communication with distant nodes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

Samiayya

Ramalingam

2021

Preprint

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In wireless sensor network (WSN), the gateways far away from the base station (BS) uses the gateways nearer to the BS to forward the data. It causes heavy traffic to the gateways in proximity with the BS. They need to manage this heavy traffic load but it leads to additional energy consumption and reduction in network lifetime. In order to overcome these issues, loads around the gateways need to be balanced. In this paper, multi objective based spider monkey optimization (MOSMO) has been presented to balance the load and to improve the network lifetime through energy efficient routing and clustering. The objective functions such as routing fitness and clustering fitness have been considered for optimal routing and clustering. The routing fitness function is found by incorporating both the minimum distance traversed by the gateways and minimum number of the gateway hops. The clustering fitness function is the minimum fitness function of gateways. The fitness function of each gateway is computed based on both the mean load of gateways as well as the distance between gateways and BS. The performance of the proposed MOSMO based routing and clustering scheme is compared with the existing Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) based routing and clustering scheme. The QoS features such as delay, energy consumption, delivery ratio, throughput and network lifetime with various node density are analyzed. The proposed work is simulated using MATLAB. The results show that, the reduction in delay and energy consumption is about 18% and 17% respectively whereas improvement in delivery ratio, throughput and network life time is about 15%, 24% and 19% respectively when compared to the existing PSO and GWO methods.

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A Grey Wolf Optimization Approach for Improving the Performance of Wireless Sensor Networks

Cited by 44 publications

References 15 publications

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Intelligent Optimization of QoS in Wireless Sensor Networks Using Multiobjective Grey Wolf Optimization Algorithm

Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

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