EEC-GA: Energy-Efficient Clustering Approach Using Genetic Algorithm for Heterogeneous Wireless Sensor Networks

Samadi, Raheleh; Seitz, Jochen

doi:10.1109/icoin53446.2022.9687275

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…The protocol leverages fitness function values derived from parameters such as the node's remaining energy, minimum distance to the base station, and neighborhood degree. The evaluation criteria encompass network stability, energy consumption, first node death, and the number of living nodes per round [28]. The developed algorithms effectively tackle the coverage problem in diverse environments [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

2024

IJATEE

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

2024

IJATEE

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“…GAs' technique design is likewise natural growth structure, and this technique was introduced in 1960 by Holland [53]. GAs depict optimized performances of heuristic, selection, and mutation along with crossover and have many implementations including the aircrafts industry [54], solar photovoltaic systems [55], heterogeneous celebrations [56], weather forecasting [57], and pharmaceutical supply chain [58]. A hybridization of GAs with any local solver can signifcantly increase the convergence, and for this purpose, SQP solver is used [59,60].…”

Section: Optimization Of Networkmentioning

confidence: 99%

Neuro‐Heuristic Computational Intelligence Approach for Optimization of Electro‐Magneto‐Hydrodynamic Influence on a Nano Viscous Fluid Flow

Butt,

Ahmad,

Raja

et al. 2023

International Journal of Intelligent Systems

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In this investigative study, the electro-magneto hydrodynamic (EMHD) influence on a nano viscous fluid model is scrutinized by designing an artificial neural network (ANN) paradigm using a neuro-heuristic approach (NHA) through the combination of GAs (genetic algorithms) and one of the most efficient locally searching solver SQP (sequential quadratic programming), i.e., NHA-GA-SQP. The fluid flow for the proposed problem is initially interpreted in the form of PDEs and then utilization of suitable similarity transformation on these PDEs yields in terms of a stiff nonlinear system of ODEs. The numerical results of the suggested fluidic model based on the variation of its physically existing parameters are calculated through the NHA-GA-SQP solver to detect the variation in velocity, thermal gradient, and concentration during the fluid flow. A detailed analysis of obtained outcomes through the NHA-GA-SQP algorithm and their comparison with the reference results estimated via the Adams method are presented. The calculation of the proposed solver’s accuracy, stability, and consistency through various statistical operators is also involved in the current inspection.

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“…To choose the ch, different combinations of energy and snbs separation were used. In order to improve the selection of ch and minimize the power usage of distant chs, a three-tier extended energy efficient clustering hierarchy (teeech) [59] method has been established. The group was generated using mfcm, or modified fuzzy-c mean clustering.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

“…HCRO-MBFSA (2020) [59] Chemical reaction optimization method for formation of cluster and selection of cluster head Homogeneous Ideal routing path is found using modified bacterial reaction.…”

Section: Table 2 Continuedmentioning

confidence: 99%

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

Preethi R

2023

IJRITCC

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In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions.

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EEC-GA: Energy-Efficient Clustering Approach Using Genetic Algorithm for Heterogeneous Wireless Sensor Networks

Cited by 8 publications

References 21 publications

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

Optimizing energy efficiency and enhancing localization accuracy in wireless sensor networks through genetic algorithms

Neuro‐Heuristic Computational Intelligence Approach for Optimization of Electro‐Magneto‐Hydrodynamic Influence on a Nano Viscous Fluid Flow

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

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