casa 19(19): e3

Research Article

A Scalable IoT Video Data Analytics for Smart Cities

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  • @ARTICLE{10.4108/eai.13-7-2018.163136,
        author={Mien Phuoc Doan and Vu The Tran and Hung Huu Huynh and Hiep Xuan Huynh},
        title={A Scalable IoT Video Data Analytics for Smart Cities},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        keywords={Scalable, video data analytics, smart city},
  • Mien Phuoc Doan
    Vu The Tran
    Hung Huu Huynh
    Hiep Xuan Huynh
    Year: 2019
    A Scalable IoT Video Data Analytics for Smart Cities
    DOI: 10.4108/eai.13-7-2018.163136
Mien Phuoc Doan1, Vu The Tran2, Hung Huu Huynh3, Hiep Xuan Huynh4,*
  • 1: Tra Vinh University. 126 Nguyen Thien Thanh Street, Ward 5, Tra Vinh City, 87000, Vietnam
  • 2: Institute for Research and Executive Education, The University of Da Nang. 158A Le Loi Street, Hai Chau District, Da Nang City, 550000, Vietnam
  • 3: Da Nang University of Science and Technology, The University of Da Nang. 54 Nguyen Luong Bang Stress, Lien Chieu District, Da Nang City, 550000, Vietnam
  • 4: College of Information and Communication Technology, Can Tho University. Campus2, 3/2 Street, Ninh Kieu District, Can Tho City, 900000, Vietnam
*Contact email:


The smart city is a comprehensive application of information resources and a high degree of information technology integration. With the technical support from IoT (Internet of things), smart city need to have three features of being instrumented, interconnected and intelligent. IoT provides the ability to manage, remotely monitor and control devices from massive streams of real-time data.Our model offers a scalable IoT video data analytics applications for Smart cities to end users, who can exploit scalability in both data storage and processing power to execute analysis on large or complex datasets. This model provides data analytics programming suites and environments in which developers and researchers can design scalable analytics services and applications. A cloud/edge-based automated video analysis system to process large numbers of video streams, where the underlying infrastructure is able to scale based on the number of camera devices and easy to integrate analytic application. The system automates the video analysis process and reduces manual intervention. The design of our model is developed to be easily extended for new kinds of IoT devices, message routing and queueing, and data analytics, to permit specific application to be programmed via the paradigm to be flexible yet simple.