UNDERSTANDING THE INFLUENCE OF NETWORK TOPOLOGY ON PERFORMANCE AND RELIABILITY IN IOT WORKSPACES

Abstract

One of the most promising technologies of the present day is the Internet of Things (IoT), which makes it possible to connect objects and systems for effective data sharing and improved user experience. However, because of the complexity of the related devices and systems, enhancing IoT networks continues to be a major problem. Numerous network optimisation concepts and techniques have been put out in an effort to address this issue and enhance the network design's resilience. Moreover, AI-driven predictive analytics can be used to reinforce network topologies in the Internet of Things. Examining the latest optimisation methods and algorithms for topological robustness in Internet of Things networks, this research draws attention to the problems and difficulties that still need to be resolved. An introduction to IoT network optimisation and the significance of topology robustness opens the paper. Then, some recently proposed optimisation techniques are covered, including particle swarm, genetic, and memetic optimisation. The essay also identifies the main obstacles to IoT network optimisation, including resource limitations, security concerns, and scalability challenges. The essay also examines the problems that impede the development of ideal solutions in this field, such as incompatibility and a lack of standardisation among devices and systems. The article concludes by outlining some possible future avenues for topological robustness research and highlighting important elements to take into account in order to improve the robustness of IoT networks. In order to further enhance IoT networks, the article also addresses intelligent solution additions like smart cities and contemporary healthcare systems.