Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT

The Internet of Things (IoT) has subsequently been applied to a variety of sectors, including smart grids, farming, weather prediction, power generation, wastewater treatment, and so on. So if the Internet of Things has enormous promise in a wide range of applications, there still are certain areas...

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Veröffentlicht in:Theoretical computer science Jg. 927; S. 87 - 97
Hauptverfasser: Khan, Mohd Abdul Rahim, Shavkatovich, Shavkatov Navruzbek, Nagpal, Bharti, Kumar, Anil, Haq, Mohd Anul, Tharini, V. Jeevika, Karupusamy, Sathishkumar, Alazzam, Malik Bader
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 26.08.2022
Schlagworte:
Cluster head
IoT
Meta-heuristic method
Whale optimization algorithm
Wireless network
Meta-heuristic method
Wireless network
Whale optimization algorithm
Cluster head
IoT
ISSN:0304-3975, 1879-2294
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Zusammenfassung:The Internet of Things (IoT) has subsequently been applied to a variety of sectors, including smart grids, farming, weather prediction, power generation, wastewater treatment, and so on. So if the Internet of Things has enormous promise in a wide range of applications, there still are certain areas where it may be improved. Designers had focused our present research on reducing the energy consumption of devices in IoT networks, which will result in a longer network lifetime. The far more suitable Cluster Head (CH) throughout the IoT system is determined in this study to optimize energy consumption. Whale Optimization Algorithm (WOA) with Evolutionary Algorithm (EA) is indeed a mixed meta-heuristic algorithm used during the suggested study. Various quantifiable metrics, including the variety of adult nodes, workload, temperatures, remaining energy, and a target value, were utilized IoT network groups. The suggested method then is contrasted to several cutting-edge optimization techniques, including the Artificial Bee Colony method, Neural Network, Adapted Gravity Simulated annealing. The findings show that the suggested hybrid method outperforms conventional methods. •Designers had focused our present research on reducing the energy consumption of devices in IoT networks, which will result in a longer network lifetime.•The suggested method then is contrasted to several cutting-edge optimization techniques, including the Artificial Bee Colony method, Neural Network, Adapted Gravity Simulated annealing.•The findings show that the suggested hybrid method outperforms conventional methods.
ISSN:0304-3975
1879-2294
DOI:10.1016/j.tcs.2022.05.031