Abstract
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.