The Contribution of Deep Learning for Future Smart Cities

Abstract

With the advancement of science and technology, the idea of smart city is not a dream anymore; rather, the world is already enjoying its benefits. Computer and information technology have come up with Internet of Things (IoT), several data analysis technologies like big data analysis, deep learning, etc., taking smart city applications to a higher level. Current smart city applications offer various facilities to ensure more effective, time-saving services like traffic management, environmental and public safety and security, energy management, healthcare services, etc. The smart city’s components firstly collect several data by using different IoT and other sensors. It requires processing those data to perform several actions to achieve the system’s benefits. Deep learning methods that use artificial intelligence to make intelligent decisions are the most widely used data analysis technique used by the applications of smart cities. The data collected from the city through IoT or sensors are processed and analyzed, and then trainings are carried out with deep learning models. As a result of this training, effective solutions can be produced for different problems, and the workload of smart city members is facilitated. In this chapter, the categories and components of smart cities are presented, the necessity and applications of deep learning in smart cities are investigated, and the possible future enhancements that could be achievable by using deep learning for smart cities are enlightened. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.