Authors - Bullet Tiwari, Reena Satput Abstract - The Internet of Things (IoT) systems are becoming numerous, trans-forming our virtual world by continually gathering information, linking equipment, and automating most of the spaces. However, the size, decentralization, and dispersal of the IoT networks cause grave droughts with reliability, security, and workability. The current rule-based surveillance tools are not able to handle the dynamism and volume of data generated by drastically many IoT devices. The paper describes a machine learning (ML) system that predicts the performance of applications, issues, and predicts failures of devices in IoT settings. It compares the methods of supervised, unsupervised, and deep learning and examines their performance in the constraints of computing power, delay and energy. It talks about the trade-offs between centralized and decentralized learning in which clouds and edges are used respectively to decide on the most suitable deployment. The framework has security, privacy, and sustainability design issues, as well. The suggested solution is expected to enhance IoT resiliency with the help of predictive intelligence to manage issues before they happen and increase the overall stability of the industry, healthcare, and smart city environments.
Open Zoom