Machine Learning and Optimization Techniques in IoT and Smart Systems: A Survey of Recent Trends

The growth of the Internet of Things (IoT) and smart systems has paved the way for the integration of machine learning (ML) and optimization techniques to enhance their efficiency, security, and scalability. This survey provides a comprehensive overview of recent advancements in applying machine learning algorithms and optimization strategies to IoT and smart systems. It highlights key areas where these technologies intersect, such as energy efficiency, intelligent decision-making, resource management, and anomaly detection. The paper discusses various machine learning approaches, including deep learning, reinforcement learning, and supervised/unsupervised models, that have been applied to optimize system performance in real-time applications. Furthermore, the survey examines optimization techniques like metaheuristics, evolutionary algorithms, and gradient-based methods that improve IoT systems' reliability and resource utilization. The integration of these techniques offers significant advantages, including reduced latency, enhanced security, and increased adaptability in smart systems. The findings emphasize the importance of adopting ML and optimization methods to address the challenges posed by the growing complexity of IoT environments, fostering future innovations in connected and autonomous systems. This survey serves as a resource for researchers and practitioners seeking to explore the synergies between IoT, ML, and optimization techniques.