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Mr. B. Ramana Reddy is an Assistant Professor in the School of Computer Science and Engineering at SVYASA Deemed to be University, Bangalore. His contribution focuses on AI, IoT, and intelligent smart-city infrastructure management.
The swift increase in urban demographics has posed considerable difficulties in overseeing transportation, electricity, water, garbage, public infrastructure, and environmental assets. Intelligent urban areas tackle these issues by incorporating Artificial Intelligence (AI), the Internet of Things (IoT), communication infrastructures, cloud computing, and data-centric decision-making frameworks. IoT devices facilitate the ongoing acquisition of real-time data from urban infrastructure, whereas AI methodologies convert this information into practical insights for forecasting, enhancement, automation, and intelligent management. This section delineates the principles, frameworks, and primary utilisations of AI and IoT in the management of smart city infrastructure. It examines IoT sensor frameworks, communication technologies, cloud and edge computing, machine learning, deep learning, and intelligent decision-support systems. Prominent domains of use encompass intelligent transportation, traffic oversight, energy infrastructures, water supply systems, waste disposal, ecological surveillance, public security, and structural integrity assessment. The section additionally explores nascent technologies including digital twins, edge artificial intelligence, federated learning, 5G/6G communications, and interpretable AI. Ultimately, issues concerning scalability, interoperability, cybersecurity, privacy, data integrity, and sustainable implementation are examined alongside prospective research avenues for creating robust, efficient, and citizen-focused smart city frameworks.
The growing reliance on integrated digital frameworks, cloud technology, Internet of Things (IoT), mobile communications, and cyber-physical systems has fostered a swiftly changing cybersecurity landscape. Traditional security methodologies reliant on fixed protocols and recognised attack signatures may encounter challenges in tackling new threats, extensive security data, and advanced attack strategies. Artificial Intelligence (AI) offers sophisticated functionalities for gathering, examining, correlating, and deciphering cybersecurity data to facilitate proactive threat intelligence and ensure safe communication. AI-powered threat intelligence frameworks can analyse information from network traffic, system logs, security notifications, vulnerability repositories, threat documentation, and more pertinent sources to detect anomalous patterns, rank risks, and facilitate prompt security choices. This section elucidates the principles, framework, methodologies, and utilisations of AI-enhanced threat intelligence and secure communication infrastructures. It addresses machine learning, deep learning, natural language processing, anomaly detection, threat forecasting, automatic alert correlation, secure communication frameworks, cryptographic safeguards, authentication, privacy protection, and AI-enhanced incident response.