The convergence of internet of things (IoT) technology and machine learning has paved the way for innovative solutions in the healthcare domain. This chapter presents an integrated approach that leverages IoT devices and advanced machine learning techniques to trace and detect healthcare-related disabilities. By continuously monitoring physiological signals and activity patterns, the proposed system enables early intervention and personalized care. The authors outline the key components of this approach, including data collection from IoT devices, data preprocessing, feature extraction, and the application of machine learning algorithms. Furthermore, ethical and privacy considerations are discussed, along with the potential benefits and challenges of deploying such a system. This chapter sheds light on the potential of IoT and machine learning to revolutionize healthcare by providing proactive and data-driven solutions for disabilities tracing and detection.
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The development of the smart grid has resulted from rising worldwide electricity demand, as well as the need to address environmental issues and improve energy efficiency. The smart grid is a cutting-edge power infrastructure that includes cutting-edge technologies to improve energy monitoring and control capabilities. To provide real-time monitoring, efficient energy management, and effective control mechanisms, this revolutionary grid system incorporates numerous components such as advanced metering infrastructure (AMI), communication networks, intelligent sensors, and data analytics. Energy monitoring and control are crucial in the smart grid, enabling utilities, operators, and consumers to manage energy resources, optimize performance, and achieve energy efficiency goals(Avancini et al., 2021; Paul Divakar et al., 2020). By monitoring consumption patterns, identifying inefficiencies, and providing real-time information, these systems enable informed decision-making and proactive management strategies. Accurate data on energy usage, supply-demand dynamics, and grid conditions enables timely actions to mitigate peak loads, reduce wastage, and ensure grid stability.
Advancements in sensor technologies, communication protocols, and data analytics have revolutionized energy monitoring in smart grids. Intelligent meters with bidirectional communication enable real-time data collection and demand response programs. Smart sensors and IoT devices provide granular data on grid parameters, equipment performance, and environmental factors. Data analytics techniques like machine learning and artificial intelligence enable predictive maintenance, load forecasting, and energy resource optimization. Real-time monitoring and control systems are essential for efficient smart grid operations, collecting, processing, and analyzing data from various sources. SCADA systems monitor grid assets, ADMS optimize operations, and ADR systems enable dynamic load management based on grid conditions and consumer preferences. These systems enable stakeholders to monitor energy flows, detect anomalies, and respond quickly to grid events(Hartono et al., 2013).
Energy monitoring and control systems in the smart grid ecosystem offer numerous benefits, including optimizing energy generation, transmission, and distribution, reducing wastage, improving grid reliability, and providing real-time insights for consumers. These systems also enable informed decisions, reduced consumption, and lower energy bills. Additionally, they support renewable energy sources and facilitate efficient energy storage management, promoting a greener, more sustainable grid. Research on smart grid technology has gained attention due to the growing demand for efficient, sustainable energy systems. These systems integrate advanced technologies for intelligent monitoring, control, and management of energy resources. This section highlights key areas of focus and advancements in smart grid technology(Abo-Zahhad et al., 2016).