Abstract: The widespread usage of Internet of Things (IoT) devices opens up new opportunities for automated operations, monitoring, and communications across various industries. However, extending the lifespan of IoT networks remains crucial because IoT devices are energy-limited. This study investigates the convergence of Graph Neural Networks (GNNs) and dominant set algorithms to extend the longevity of IoT networks. GNNs are neural networks that capture complex relationships and node interactions based on graph-structured data. With these capabilities, GNNs are extremely effective at modeling IoT network dynamics, where devices are connected and whose interactions have a significant impact on performance. In contrast, dominant set algorithms are defined as an approach in which nodes of a network function as agents or leaders to perform resource-efficient and resource-distributed communication. A further detailed overview leverages existing techniques to describe GNNs' role in optimizing dominant set algorithms and discusses integrating these technologies into addressing energy efficiency challenges in IoT settings.

Dezhi Liao and Xueming Huang, “Graph Neural Networks and Dominant Set Algorithms for Energy-Efficient Internet of Things Environments: A Review” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151094

@article{Liao2024,
title = {Graph Neural Networks and Dominant Set Algorithms for Energy-Efficient Internet of Things Environments: A Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151094},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151094},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Dezhi Liao and Xueming Huang}
}