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An efficient multi-objective framework for wireless sensor network using machine learning
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DOI：10.1038/s41598-025-89101-1.png)
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摘要
En 中文
A wireless sensor network (WSN) is an evolving field in the modern real world, a collection of spatially dispersed distributed and dedicated sensor nodes. It enables data transmission between Internet of Things (IoT) devices, even without human interaction. It connects the sensor nodes to a central gateway, where information can be collected from each sensor node and transmitted to the user for further processing. However, it is used everywhere, from event monitoring to sensing the environment, personal and home area networks, and other service-oriented networks. Due to the heterogeneous nature of devices in the IoT networks, efficient use has become a challenging issue. Several IoT benchmark problems are available in the literature, and many researchers try to solve these problems. Thus, researchers have conducted many studies to obtain an optimal solution for this problem, but there is still a research gap. Hence, an efficient model is proposed and tested using 25 benchmark IoT problems to address this problem. The results obtained in this paper reveal that the proposed model provides a better solution.
Keyword:
Multi-objective optimization (MOO)
Internet of things (IoT)
Bench-mark IOT problems evolutionary algorithm (EA)
High-performance computing (HPC)
Wireless sensor network (WSN)
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期刊
IF:
3.9
论文数:
28.0W
被引数:
83.5W
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