Harnessing Nature-Inspired Algorithms for Energy-Efficient Artificial Intelligence of Things
Ud Din, Ikram Khan, Kamran Habib Almogren, Ahmad Guizani, Mohsen
Ud Din, Ikram
Khan, Kamran Habib
Almogren, Ahmad
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
This paper aims at analyzing and comparing an adaptive algorithm-based method for improving the performance of Internet of Things (IoT) systems through simulation studies. Concentrating on active and complex scenarios, the study presents new proposals for secure and smart learning of routes, activity forecasting for nodes, link stability estimation, and flexible resource management. These methods are benchmarked against conventional algorithms to evaluate the effectiveness of the proposed solution based on routing efficiency, traffic prediction, link, resource consumption, network response time, and energy requirements. The findings are encouraging, the adaptive algorithms do improve dramatically on the standard ones making the system slower and consuming much less power. From the findings of the study it can be concluded that using adaptive algorithms in IoT can have a high impact in terms of improvement in efficiency as well as sustainability. We conclude this work by providing some directions for further research and development in the IoT field.
Citation
I. U. Din, K. H. Khan, A. Almogren and M. Guizani, "Harnessing Nature-Inspired Algorithms for Energy-Efficient Artificial Intelligence of Things," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2024.3520714
Source
IEEE Internet of Things Journal
Conference
Keywords
Internet of Things, Artificial intelligence, Heuristic algorithms, Routing, Prediction algorithms, Optimization, Energy consumption, Adaptive systems, Vehicle dynamics, Resource management
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Publisher
IEEE