Читать книгу Cyberphysical Smart Cities Infrastructures. Optimal Operation and Intelligent Decision Making онлайн

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To develop the smart healthcare system, we need to use healthcare networks that are the inter‐ and intra‐connection among the healthcare components like IoT devices and sensors to enhance the process of monitoring and consequent services for patients [4, 13]. The performance of such a system heavily depends on the quality of this network communication or online synchronization with other associated networks (other smart systems in smart cities) that actively contribute to the service operation in a positive way. For example, a healthcare network may take advantage of networks' resources, like energy, in case the system fails to run an operation due to the lack of enough power supply. In this situation, the best solution is connecting this network with other networks for the benefit of both patients and their health and service management reliability [4].

In the processes of making and turning cities into smart cities, we can identify several representative features. According to a research study [14], the scientists investigated a period of 10 years of work from 2008 to 2018 and discovered that smart cities might have some features in common. The most interesting features in ssss1 [9, 14, 15] are smart economy, smart people, smart governance, smart mobility, smart environment, smart energy, and smart living that are shown in ssss1. As shown in ssss1, although each feature represents the importance of itself to smart cities, they all are interrelated to each other. Each feature has direct or indirect impact on others. Furthermore, proposed decision‐making solutions used in smart cities are multi‐criteria decision making (MCDM), mathematical programming (MP), AI, and integrated method (IM). In this study, we aim to discover AI solutions, particularly machine learning and deep learning (DL) approaches rather than others, for smart mobility.