The rapid growth of urban populations worldwide imposes new challenges on citizens’ daily lives, including environmental pollution, public security, road congestion, etc. New technologies have been developed to manage this rapid growth by developing smarter cities. Integrating the Internet of Things (IoT) in citizens’ lives enables the innovation of new intelligent services and applications that serve sectors around the city, including healthcare, surveillance, agriculture, etc. IoT devices and sensors generate large amounts of data that can be analyzed to gain valuable information and insights that help to enhance citizens’ quality of life. Deep Learning (DL), a new area of Artificial Intelligence (AI), has recently demonstrated the potential for increasing the efficiency and performance of IoT big data analytics. In this survey, we provide a review of the literature regarding the use of IoT and DL to develop smart cities. We begin by defining the IoT and listing the characteristics of IoT-generated big data. Then, we present the different computing infrastructures used for IoT big data analytics, which include cloud, fog, and edge computing. After that, we survey popular DL models and review the recent research that employs both IoT and DL to develop smart applications and services for smart cities. Finally, we outline the current challenges and issues faced during the development of smart city services.

世界范围内城市人口的快速增长给公民的日常生活带来了新的挑战，包括环境污染，公共安全，道路拥堵等。人们已经开发出新技术来通过发展更智能的城市来应对这种快速增长。将物联网（IoT）融入市民生活中，可以创新为城市的各个部门提供服务的新型智能服务和应用程序，包括医疗保健，监视，农业等。物联网设备和传感器生成大量可以分析的数据获得有价值的信息和见解，有助于提高公民的生活质量。深度学习（DL）是人工智能（AI）的新领域，最近展示了提高IoT大数据分析效率和性能的潜力。在这项调查中 我们提供了有关使用物联网和DL来发展智慧城市的文献的综述。我们首先定义物联网并列出物联网生成的大数据的特征。然后，我们介绍用于物联网大数据分析的不同计算基础架构，包括云，雾和边缘计算。之后，我们调查了流行的DL模型，并回顾了最近使用IoT和DL为智能城市开发智能应用程序和服务的研究。最后，我们概述了智慧城市服务开发过程中当前面临的挑战和问题。其中包括云，雾和边缘计算。之后，我们调查了流行的DL模型，并回顾了最近使用IoT和DL为智能城市开发智能应用程序和服务的研究。最后，我们概述了智慧城市服务开发过程中当前面临的挑战和问题。其中包括云，雾和边缘计算。之后，我们调查了流行的DL模型，并回顾了最近使用IoT和DL为智能城市开发智能应用程序和服务的研究。最后，我们概述了智慧城市服务开发过程中当前面临的挑战和问题。