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| Title | Optimization Of Network Topology Robustness In Iots: A Systematic Review |
|---|---|
| ID_Doc | 40645 |
| Authors | Changazi S.A.; Bakhshi A.D.; Yousaf M.; Mohsin S.M.; Akber S.M.A.; Abazeed M.; Ali M. |
| Year | 2024 |
| Published | Computer Networks, 250 |
| DOI | http://dx.doi.org/10.1016/j.comnet.2024.110568 |
| Abstract | Internet of Things (IoT) has emerged as one of the most promising technologies of the modern era, enabling connectivity between devices and systems for efficient data exchange and enhanced user experience. However, the optimization of IoT networks remains a significant challenge due to the complexity of the interconnected devices and systems. To overcome this challenge, various techniques to optimize networks and algorithms for enhancing the robustness of the network topology have been proposed. Artificial intelligence is also used to achieve the robustness of network topologies in IoT through AI-driven predictive analytic. This article reviews the state-of-the-art optimization techniques and algorithms for topology robustness in IoT networks, highlighting the open issues and challenges in this field. The article begins by providing an overview of IoT network optimization and the need for topology robustness. It then discusses various optimization algorithms proposed in recent years, such as particle swarm optimization, memetic algorithms and genetic algorithms, among others. The article also highlights the key challenges in optimizing IoT networks, such as scalability, resource constraints, and security issues. Moreover, the article explores the open issues in this field, such as the lack of standardization and interoperability between devices and systems, which hinder the development of optimal solutions. Finally, the article identifies specific research directions in the area of topology robustness, emphasizing the vital factors to consider enhanced robustness for IoT networks. Additionally, the article discusses the enhancements for intelligent solutions, like smart cities and modern healthcare systems, which can further improve the optimization of IoT networks. © 2024 Elsevier B.V. |
| Author Keywords | Artificial intelligence; Challenges in IoT optimization; Genetic and predictive algorithms; Intelligent solutions; Internet of Things (IoT); Optimized networks; Topology robustness |
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