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Dr. Renu Bagoria is a Professor in the Faculty of Engineering and Technology at Jagannath University, Jaipur, Rajasthan, India. She is actively engaged in teaching, research, and academic development in the fields of engineering and emerging technologies. Her research interests include Artificial Intelligence, the Internet of Things (IoT), intelligent monitoring systems, data analytics, and smart computing applications. She is committed to promoting interdisciplinary research and developing innovative technology-driven solutions for real-world applications.
The Internet of Things (IoT) has surfaced as a pivotal technology for facilitating astute surveillance and anticipatory decision-making in numerous practical applications. IoT systems perpetually gather and assess real-time data from physical surroundings by integrating sensors, intelligent devices, communication networks, cloud computing infrastructures, and Artificial Intelligence (AI). The amalgamation of Machine Learning (ML), Deep Learning (DL), Edge Computing, Big Data Analytics, and Cloud Computing facilitates intelligent surveillance, anomaly identification, predictive upkeep, environmental forecasting, healthcare oversight, industrial automation, intelligent agriculture, transportation administration, and smart city initiatives. IoT-integrated smart monitoring systems enhance operational efficacy, resource allocation, system dependability, and decision-making assistance, all while decreasing operating expenses and mitigating hazards. This chapter delineates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues of IoT-facilitated intelligent monitoring and forecasting systems.
Artificial intellect (AI) has emerged as a crucial technology for augmenting the intellect, flexibility, and independence of contemporary robotic systems. Conventional robots were mostly created to carry out specific and repetitive tasks, but AI-driven robots possess the ability to sense their environment, learn from information, make autonomous judgements, adjust to dynamic conditions, and undertake intricate activities with minimal human involvement. The amalgamation of machine learning, deep learning, computer vision, natural language processing, sensor technologies, and reinforcement learning has greatly enhanced the functionalities of robotics and autonomous systems. This chapter elucidates the essential principles, frameworks, technologies, and utilisations of Artificial Intelligence in robotics and autonomous systems. Ultimately, issues concerning safety, reliability, cybersecurity, ethical considerations, computational demands, and practical implementation are examined, alongside prospective research avenues for intelligent and dependable autonomous systems.