| Title |
Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions |
| ID_Doc |
41869 |
| Authors |
Ben Atitallah, S; Driss, M; Boulila, W; Ben Ghézala, H |
| Title |
Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.cosrev.2020.100303 |
| 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. (C) 2020 Elsevier Inc. All rights reserved. |
| Author Keywords |
Internet of Things; Deep Learning; Smart city; Big data analytics; Review |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000594311100001 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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