AI Inference for Smart Cities Integrating IoT and Big Data G. Muthupandi, S. Pavithra, R. Vikram, K. Jayakumar, B. Vishnu, et al. Harnessing AI Inference for Intelligent Decision Making in Real Time Dataflows, 2026 The 21 st century has increased the rate of urbanization, thus making the problems of traffic snarl up, pollution, energy crisis, improper waste disposal and heavy load on the social safety and health system more complex. The idea of the smart city became one of the new technologies that integrate Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data in order to enhance the efficiency and quality of life in cities. In particular, AI inference enables real-time decision making based on data in major sectors. The examples of successful ones are such cities as Barcelona, Amsterdam, Chicago, Singapore, Los Angeles and Seoul. Other advantages of sustainability and disadvantages like privacy, cybersecurity, cost of infrastructure, and digital inequality are also discussed in the chapter.
Case Studies: AI Inference Applications in IoT and Big Data Domains G. Muthupandi, S. Pavithra, R. Nandhakumar, P. Vidhya Lakshmi, R. Vikram, et al. Harnessing AI Inference for Intelligent Decision Making in Real Time Dataflows, 2026 The paper will explore how AI inference on IoT and Big Data is transformative with respect to the health care sector, manufacturing, smart cities, energy, and agriculture. AI inference transforms sensor data into actionable intelligence through the combination of real-time data, predictive analytics, and automation, and enhances efficiency, sustainability, and reliability. Applications in predictive maintenance, smart grids, urban management and precision farming are pointed out in the discussion. The most important issues, such as the quality of data, privacy, scalability, and ethical use are discussed as well as ways of dealing with them. New tendencies such as edge computing, federated learning, explainable AI, 5G integration, and digital twins are found to be pushing the next generation of smart, connected systems. In general, the chapter stresses the importance of AI inference as a source of data-experienced innovation and as the means of developing more intelligent, sustainable, and resilient structures to climate change in the future.
