AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Simulations under different cases confirm that the designed HTDAA-based healthcare data management system achieves more robust and energy-efficient solutions than the traditional models.
The rapid advancement of Internet of Things (IoT) and Cloud Computing has created significant opportunities in the healthcare sector. This paper proposes Health Cloud, an intelligent IoT-based healthcare monitoring system using cloud technology for real-time patient monitoring. The proposed system collects vital health parameters such as heart rate, body temperature, blood pressure, and oxygen level using IoT-enabled sensors and transmits the data to a cloud platform for storage and analysis. The cloud-based approach enables remote access to patient information and supports efficient healthcare decision-making. The system provides continuous monitoring, early detection of health abnormalities, and improved patient care. The integration of IoT and cloud technologies reduces manual monitoring efforts and enhances the efficiency of healthcare services.
The rapid growth of the Internet of Medical Things (IoMT) has created significant opportunities for developing intelligent remote healthcare systems capable of continuous patient monitoring. However, many existing low-cost healthcare monitoring solutions are limited by single-parameter sensing, poor interoperability, inadequate cloud integration, and restricted scalability for multi-patient applications. This paper presents the design and development of an edge-enabled IoT-based smart healthcare monitoring platform using the Raspberry Pi Pico W for real-time acquisition, processing, and cloud storage of physiological data. The proposed system integrates three biomedical sensors, namely the MAX30102 sensor for heart rate and blood oxygen (SpO₂) monitoring, the DS18B20 digital sensor for body temperature measurement, and the AD8232 analog front-end for electrocardiogram (ECG) signal acquisition. The embedded edge node performs local signal preprocessing, physiological parameter extraction, timestamp synchronization, and secure wireless transmission of patient data to a cloud database through REST-based communication over Wi-Fi. A cloud-centric architecture is developed using Supabase to provide reliable data storage, multi-node scalability, and seamless integration with future intelligent healthcare applications. The proposed framework supports continuous remote patient monitoring while maintaining a low hardware cost, reduced computational overhead, and efficient data communication. Experimental implementation demonstrates reliable real-time physiological data acquisition, stable cloud synchronization, and effective management of multimodal healthcare data suitable for subsequent analytical and predictive applications. The developed platform establishes a scalable foundation for next-generation smart healthcare systems by combining low-cost embedded hardware, edge computing, and cloud technologies to improve accessibility, reliability, and remote medical monitoring.
Research on low-power Internet of Things (IoT) systems has gained significant momentum within the broader context of green and sustainable IoT. In this setting, batteryless (BL) IoT has emerged as a promising solution to reduce maintenance costs and environmental impact by eliminating the need for battery replacements. As large-scale IoT deployments for monitoring and sensing applications continue to expand, important challenges arise in the design and operation of sustainable BL IoT networks, including long-term reliability, performance evaluation, and the analysis of energy-harvesting behavior under real-world conditions. To help address these challenges, this article presents a comprehensive dataset capturing the behavior of BL IoT devices deployed in an indoor environmental sensing network. The dataset comprises 100 days of measurements collected from sensors installed throughout an office building and includes data from two types of IoT devices: 1) plugged-in (PI) sensors with continuous power supply; and 2) BL sensors powered exclusively by energy harvested from indoor light. This dual-device deployment enables direct comparison of sensing performance, reliability, and energy dynamics between powered and energy-harvesting systems. The final dataset contains over three million samples collected from 28 sensors (14 PI and 14 BL) deployed across approximately 300 m<inline-formula><tex-math notation="LaTeX">${}^{2}$</tex-math></inline-formula> of office space spanning nine rooms. The dataset provides a valuable resource for the systematic investigation of BL IoT systems, enabling rigorous analysis of deployment strategies, spatial–temporal sensing dynamics, energy-harvesting behaviors, system performance, and long-term sustainability considerations in next-generation self-powered IoT sensing networks.</p> <p><bold>IEEE SOCIETY/COUNCIL</bold> Communications Society (COMSOC)</p> <p><bold>DATA TYPE/LOCATION</bold> Comma Separated Values (CSV); KU Leuven, Belgium</p> <p><bold>DATA DOI/PID</bold> <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.21227/TF0N-SB66">10.21227/TF0N-SB66</ext-link>
Internet of Things (IoT) is a technology link to the objects that interact between themselves to provide better services for users in an unbelievable way and makes everything easy. The applications of IOT include healthcare monitoring, smart city, smart retail, smart home and agriculture. Wireless Sensor Networks (WSN) plays a vital role in IoT as it includes data processing, connectivity, sensing, and data acquiring. A wireless, self-configuring, and multi-hop network is called a mobile ad-hoc network (MANET). The node of the MANET acts as a router and is always connected to WSN. Hence, the interface between IoT and MANET opens new ways for service facilities in smart environments. The main challenging issue of MANET-IOT is energy balancing over nodes. The IoT technology works on various wireless sensors and so the main aim of MANET protocols is to find the shortest path to be more efficient. Many researchers have designed energy-efficient routing protocols for improving network lifetime and due to the lack of composite solution over MANET-IoT networks, we plan to review the novel energy-efficient routing protocols that can provide a composite solution for routing over the MANET-IoT networks. This survey reviews the different types of routing protocols based on its topological structure, and energy efficiency characteristics. The performance metrics used to evaluate the efficiency of the routing protocols are also presented.
This paper presents a comprehensive review of IoT-based smart health risk prediction systems that integrate Artificial Intelligence (AI), biomedical sensors, and Internet of Medical Things (IoMT) technologies for advanced healthcare monitoring and chronic disease management. The study discusses the architecture of IoMT systems, which utilize wearable and implantable sensors to continuously collect physiological and biomedical data from patients in real time. These data are analyzed using Machine Learning (ML) and Deep Learning (DL) algorithms for disease prediction, classification, and early diagnosis of conditions such as cardiovascular diseases, diabetes, and respiratory disorders. The review highlights the major components of IoMT systems, including data acquisition, wireless data transmission, intelligent data processing, and healthcare user interfaces for clinical decision-making. Communication technologies such as Bluetooth, Wi-Fi, and edge computing are discussed for enabling efficient and low-latency healthcare monitoring. The paper also examines interoperability standards including HL7 and FHIR for secure and scalable healthcare data exchange. Furthermore, major challenges associated with IoMT healthcare systems, such as data privacy, cybersecurity, interoperability, scalability, and AI bias, are critically analyzed. Emerging technologies including Federated Learning, blockchain, Explainable AI (XAI), and energy-efficient wearable sensors are also reviewed as future research directions for intelligent healthcare systems. The review demonstrates that AI-integrated IoMT systems can importantly improve remote patient monitoring, predictive healthcare analytics, and personalized medical services.
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