Machine Learning and Internet of Things for Smart Cities: Technologies, Applications, and Challenges
Abstract
The convergence of Machine Learning (ML) and the Internet of Things (IoT) has emerged as a key technological foundation for developing intelligent and sustainable smart cities. IoT networks generate massive volumes of data through sensors and connected devices, while ML algorithms provide the analytical capabilities required to transform this data into actionable intelligence. This review examines the current state of ML-enabled IoT applications in smart cities, focusing on intelligent transportation, traffic prediction, energy management, waste management, environmental monitoring, public safety, and smart infrastructure. Different machine learning approaches used for analyzing IoT-generated data are reviewed, including supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of edge and fog computing in reducing latency and improving the scalability of intelligent IoT systems is also discussed. In addition, the review identifies critical challenges involving cybersecurity, data privacy, interoperability, computational requirements, communication reliability, and energy efficiency. Emerging technologies such as federated learning, digital twins, 5G/6G communication, and edge AI are examined as potential solutions. The review provides a consolidated research perspective and identifies future opportunities for creating secure, autonomous, sustainable, and data-driven smart cities.
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