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Smart city article details
| Title | Internet Of Medical Things (Iomt) For Smart City |
|---|---|
| ID_Doc | 32864 |
| Authors | Mirmahaleh S.Y.H. |
| Year | 2025 |
| Published | Digital Twin, Blockchain, and Sensor Networks in the Healthy and Mobile City |
| DOI | http://dx.doi.org/10.1016/B978-0-443-34174-8.00016-8 |
| Abstract | Smart city (SC) deployment is essential to meet the modern requirements of human life across various sectors such as education, business, management, industry, and healthcare. In particular, the medical sector plays a crucial role in addressing the healthcare needs of individuals, including healthcare services, medicine requirements, and advanced treatment techniques in both normal and high-risk situations. To effectively manage these medical demands within a SC framework, a robust communication platform is necessary to connect a wide range of smart e-health devices and services. This platform must accommodate different asynchrony patterns while considering key parameters like energy efficiency and communication delay to support Internet of Medical Things (IoMT)-based applications and power management. Additionally, ensuring security and dependability is paramount in this context. The chapter discusses various techniques that can enhance the integration of IoMT within a SC environment, including digital twins (DTs) and augmented reality-based services in a metaverse space. Hardware-based methods are explored to facilitate communication between medical devices at the physical layer of IoMT, aligning with the sensing or infrastructure layer of an SC. These methods encompass hardware-based deep learning accelerators, flow mapping techniques, local communication infrastructures, partitioning, and clustering approaches. Furthermore, the chapter delves into the utilization of modern algorithms such as q-learning, evolutionary computing, and human brain-inspired algorithms to address the complexity of IoMT within a metaverse-based SC. By considering hardware, hardware–software, and combined artificial intelligence-hardware approaches, the study aims to optimize the deployment of IoMT applications and enhance communication among them in a SC setting. Simulation results based on patterns and models are presented to demonstrate the efficacy of these techniques in supporting IoMT applications within an SC. Overall, the insights provided in this chapter offer valuable guidance for leveraging hardware and AI-driven strategies to enhance healthcare delivery and connectivity in a SC environment. © 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. |
| Author Keywords | clustering; digital twins (DT); flow mapping; Internet of Medical Things (IoMT); local communication infrastructures (LCI); smart city (SC) |
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