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Mr. R. Ananthakrishnan is an Assistant Professor in the Department of Electronics and Communication Engineering at Dhaanish Institute of Technology, Coimbatore, India. His research interests include Wireless Communication, IoT, 5G/6G Networks, Cloud Computing, and Smart Communication Systems.
The advent of Sixth Generation (6G) communication networks is anticipated to transform wireless connectivity by delivering ultra-high data rates, minimal latency, intelligent automation, and uninterrupted connectivity among billions of devices. The amalgamation of Cloud Computing with the Internet of Things (IoT) is an essential basis for actualising the concept of 6G-enabled smart ecosystems. Cloud platforms provide scalable processing, storage, and analytical capabilities, whereas IoT devices provide substantial volumes of real-time data from various applications, including smart cities, healthcare, transportation, industrial automation, and environmental monitoring. This chapter examines the architecture, technologies, frameworks, and applications of Cloud-IoT integration in 6G communication networks. It addresses enabling technologies such as edge computing, artificial intelligence, network slicing, digital twins, and intelligent resource management. This chapter analyses security difficulties, implementation concerns, and prospective research avenues for the advancement of intelligent, scalable, and sustainable 6G communication infrastructures.
warning mechanisms. It investigates disaster detection, the amalgamation of diverse environmental data, predictive analytics through machine learning and deep learning, spatiotemporal modelling, anomaly identification, uncertainty assessment, hazard-specific agents, coordination among multiple agents, edge and cloud computing intelligence, integration of satellite and remote sensing, geospatial analysis, and the dissemination of intelligent warnings. The section also explores elucidation, human-in-the-loop decision processes, cybersecurity, data management, communication dependability, and ethical implications. A conceptual framework is suggested wherein specialised agents perpetually observe threats, employ relevant analytical models, assess forecasts, and disseminate evidence-based alerts within established operational parameters. Future trajectories encompassing digital twins, multimodal foundational models, autonomous sensing, federated learning, and cooperative multi-agent disaster management systems are likewise examined. The section underscores that Agentic AI ought to enhance traditional scientific forecasting and emergency management frameworks instead of functioning as an unbridled autonomous entity.