| Abstract |
The confluence of artificial intelligence (AI) and the Internet of Things (IoT) has led to the expansion of smart healthcare systems that use cloud and fog computing to identify and predict diseases. Wireless body area networks (WBANs), which comprise several sensors and a coordinator node, function as IoT healthcare components in these networks. Although a single user’s WBAN can send data straight to the cloud, multi-hop routing is required inside densely distributed WBANs in hospitals that manage groups of patients. These wireless sensor networks present routing issues because they operate in an unpredictable, dynamic, and irregular communication environment. This chapter presents two AI-enabled algorithms that use fuzzy expert systems and reinforcement learning: the fuzzy real-time search algorithm and the fuzzy adaptive tree routing algorithm. These algorithms optimize routing by considering variables including remaining energy, strength of received signals, packet drop rate, and receiving time, along with fuzzy logic-based link cost estimation. These AI-driven methodologies provide essential solutions for routing optimization in IoT-enabled healthcare frameworks inside smart hospitals in the context of smart city scenarios. They guarantee energy efficiency, consistency, accuracy, real-time compatibility, and quality of service in data-driven smart healthcare systems. © 2025 selection and editorial matter, Diptendu Sinha Roy, Mir Wajahat Hussain, K. Hemant Kumar Reddy, Deepak Gupta; individual chapters, the contributors. |
