Deep Smart Cities: A Review of Smart Cities Applications-Based an Inference of Deep Learning Upon IoT Devices

Abstract

Recent years have witnessed an increase in the use of IoT devices in Smart Cities (SC). With the remarkable success of integrating deep learning (DL) in various fields of SC such as smart environment, smart agriculture, and more. There is an emerging field to deploy DL into IoT devices to meet the requirement of SC applications as real-time applications and lightweight computation. However, the limited resource of IoT devices poses a challenge to fulfilling the DL models that demand large storage and massive computation. The aim of this paper is focused on the integration of DL with IoT in SC and conducts up-to-date state-of-the-art studies on the SC applications. First, a present overview of SC, IoT and DL. Second, we review studies that integrate DL and IoT into four SC applications (smart environment, smart transportation, smart agriculture, and smart home). It covers a series of crucial applications in SC such as Air pollution, water pollution, autonomous driving, activity recognition and detection, indoor localization, and more. Third, present the main challenges once integrate DL on IoT devices in SC. Lastly, we introduced open issues for the future direction obtained when integrate DL models with IoT devices in SC applications to inspire further research in this area.