Читать книгу Cyberphysical Smart Cities Infrastructures - Группа авторов - Страница 21

Current advanced technologies in sensors and IoT devices make it essential in leveraging AI, particularly machine learning, to model the data for further application [16, 17]. The IoT devices are considered as the most important and unavoidable parts of smart cities. These devices provide a huge amount of data depending on which applications are going to be used, such as healthcare and transportation applications [18].

IoT technologies have proliferated in many fields, such as smart healthcare [13, 19] and smart home systems like Alexa [20, 21], specifically in urban cities, turning them into smart cities. Thus, the huge usage of IoT technologies plays a pivot role in generating big data that requires solutions to analyze and keep track of smart cities' activities. This big data analysis [22] provides invaluable knowledge to integrate all smart cities' sources like IoT and networks. As smart cities and their data grow, this analysis process may become challenging for future decision making. In Section 2.3, we address this problems and discuss possible solutions that have been proposed thus far. Researchers in [23] proposed a new solution to leverage citizen‐centered big data analysis to apply to smart cities, which is to determine a path for implementation of citizen‐centered big data analysis for the sake of decision making. This solution's main goal is to provide imperative perspectives: data analysis algorithms and urban governance issues [23].

Furthermore, researchers in [24] provided several DL applications in smart cities, such as smart governance, smart urban modeling, smart education, smart transportation, intelligent infrastructures, and smart health solutions. Additionally, the challenges of using DL toward smart cities are also addressed. However, still the problem of decision making in smart cities remains challenging. In Section 2.3, we address the problems and highlight the possible solutions.