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

Ajmi, Nader; Helali, Abdelhamid; Lorenz, Pascal; Mghaieth, Ridha

doi:10.3390/s21030791

Cited by 41 publications

(24 citation statements)

References 57 publications

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“…GA is typically used to control the results of the precise population for solving various steep/complicated models based on optimal training. In recent years, GA is implemented in the shotgun metabolomics [ 29 ], wellhead back pressure control system [ 30 ], bearing fault diagnosis of induction motors [ 31 ], energy efficient clustered wireless sensor networks [ 32 ], beam deflection monitoring systems [ 33 ], adjustment problem of sensor acquisition frequency [ 34 ], image processing optimization tasks [ 35 ], and torque adjustment for the ankle push-off in the walking bipedal robots [ 36 ]. The optimization performance in terms of efficiency, accuracy, and viability of GAs is further enhanced by introducing the concept of hybridization with efficient local search.…”

Section: Methodsmentioning

confidence: 99%

Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks

Nisar

Sabir

Raja

et al. 2021

Sensors

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In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local search interior-point algorithm (IPA), i.e., ANN-GA-IPA. The environmental and economic system is dependent of three categories, execution cost of control standards and new technical diagnostics elimination costs of emergencies values and the competence of the system of industrial elements. These three elements form a nonlinear differential environmental and economic system. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions.

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Section: Methodsmentioning

confidence: 99%

Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks

Nisar

Sabir

Raja

et al. 2021

Sensors

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show abstract

“…In this paper, the energy model is designed and which computing the energy consumption of WSN structure. The WSN architecture is specially designed with two different types of communication channels such as multipath channel based on the distance among transmitter and receiver and free space channel [21]. The distance of the channels is always lower than the reference free space model which is always utilize.…”

Section: Energy Modelmentioning

confidence: 99%

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

Karunkuzhali¹,

Meenakshi²,

Lingam

2021

Preprint

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In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and Internet of Things (IoT) is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, Adaptive Fuzzy C means clustering and Seagull Optimization Algorithm (AFCM-SOA) is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of Fuzzy C Means Clustering and Seagull Optimization Algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The Fuzzy C-Means Clustering is utilized to empower the energy efficient operation and load balancing. In the Fuzzy C-Means Clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as Atom Search Optimization (ASO) and Emperor Penguin Optimization (EPO) respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

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“…Wireless sensor networks (WSNs) are multihop wireless networks composed of sensor nodes deployed in the detection area in a wireless self-assembling manner [1,2]. It can be defined as a network of tiny, small, expensive, and highly intelligent devices of sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks

Gou

2021

Wireless Communications and Mobile Computing

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With the popularity of swarm intelligence algorithms, the positioning of nodes to be located in wireless sensor networks (WSNs) has received more and more attention. To overcome the disadvantage of large ranging error and low positioning accuracy caused by the positioning algorithm of the received signal strength indication (RSSI) ranging model, we use the RSSI modified by Gaussian to reduce the distance measurement error and introduce an improved whale optimization algorithm to optimize the location of the nodes to be positioned to improve the positioning accuracy. The experimental results show that the improved whale algorithm performs better than the whale optimization algorithm and other swarm intelligence algorithms under 20 different types of benchmark function tests. The positioning accuracy of the proposed location algorithm is better than that of the original RSSI algorithm, the hybrid exponential and polynomial particle swarm optimization (HPSO) positioning algorithms, the whale optimization, and the quasiaffine transformation evolutionary (WOA-QT) positioning algorithm. It can be concluded that the cluster intelligence algorithm has better advantages than the original RSSI in WSN node positioning, and the improved algorithm in this paper has more advantages than several other cluster intelligence algorithms, which can effectively solve the positioning requirements in practical applications.

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

Cited by 41 publications

References 57 publications

Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks

Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks

An Adaptive Fuzzy C Means With Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field With IoT

A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks

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