review-article

Leveraging Deep Learning and IoT big data analytics to support the smart cities development: : Review and future directions

Authors:

Safa Ben Atitallah,

Henda Ben GhézalaAuthors Info & Claims

Volume 38, Issue C

https://doi.org/10.1016/j.cosrev.2020.100303

Published: 01 November 2020 Publication History

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.

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cover image Computer Science Review

Computer Science Review Volume 38, Issue C

Nov 2020

528 pages

ISSN:1574-0137

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Published: 01 November 2020

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https://dl.acm.org/doi/10.4018/JOEUC.354707
Wang TFang WZhang W(2024)Access Control for IoT-based Big Data: a State-of-the-art ReviewProceedings of the 2024 6th International Conference on Big Data Engineering10.1145/3688574.3688586(81-87)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3688574.3688586
Zhang HZhang JWang RHuang YZhang MShang XGao C(2024)Smart carbon monitoring platform under IoT-Cloud architecture for small cities in B5GWireless Networks10.1007/s11276-021-02756-230:5(3837-3853)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11276-021-02756-2
Amiri ZHeidari ANavimipour NEsmaeilpour MYazdani Y(2024)The deep learning applications in IoT-based bio- and medical informatics: a systematic literature reviewNeural Computing and Applications10.1007/s00521-023-09366-336:11(5757-5797)Online publication date: 1-Apr-2024
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Ben Atitallah SDriss MBoulila WKoubaa A(2024)Strengthening Network Intrusion Detection in IoT Environments with Self-supervised Learning and Few Shot LearningComputational Collective Intelligence10.1007/978-3-031-70819-0_7(83-96)Online publication date: 9-Sep-2024
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Nag AHassan MDas ASinha AChand NKar ASharma VAlkhayyat A(2024)Exploring the applications and security threats of Internet of Thing in the cloud computing paradigmTransactions on Emerging Telecommunications Technologies10.1002/ett.489735:4Online publication date: 8-Apr-2024
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Li XCui ZZhang FLi L(2023)Application of Big Data Privacy Protection Based on Edge Computing in the Prediction of Martial Arts Training Movement TrajectoryInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.33422618:1(1-14)Online publication date: 28-Nov-2023
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Guo JNazir S(2021)Internet of Things Based Intelligent Techniques in Workable ComputingScientific Programming10.1155/2021/68051042021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6805104

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