Research Article
A Scalable IoT Video Data Analytics for Smart Cities
@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}, volume={6}, number={19}, publisher={EAI}, journal_a={CASA}, year={2019}, month={11}, keywords={Scalable, video data analytics, smart city}, doi={10.4108/eai.13-7-2018.163136} }
- Mien Phuoc Doan
Vu The Tran
Hung Huu Huynh
Hiep Xuan Huynh
Year: 2019
A Scalable IoT Video Data Analytics for Smart Cities
CASA
EAI
DOI: 10.4108/eai.13-7-2018.163136
Abstract
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.
Copyright © 2019 Mien Phuoc Doan et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.