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International Journal of Frontiers in Engineering Technology, 2022, 4(4); doi: 10.25236/IJFET.2022.040402.

The Design of Environment Monitoring Systems Based on Digital Twins in Smart Home


Baichun Bing1, Shunhao Yan2

Corresponding Author:
Baichun Bing

1Beijing University of Chemical Technology, Beijing, China

2Beijing University of Posts and Telecommunications, Beijing, China


Nowadays, Digital Twins (DT) and Smart Home are both hot points. As the recent new idea, the former one has received great attention. And after few years developing, the latter one is gradually maturing. In principal, in a typical smart home, there should be numerous devices and systems which most can be networked. Using various sensors and actuators, these system could collect the data from the real world and influence the real world. In fact, DT can also be used in these systems. By using DT, we could cheek not only the current states of the system, but also the history of the data for learning. Therefore, it could be the connection between smart home and AI. This article firstly introduces the development status of smart home and DT. Then it analyzes the possibility of connecting smart home and DT. Finally the article pays attention to the framework and design for the whole system and introduces the modeling methods, the computing methods and system optimization. Finally, the article makes a conclusion and draw the picture of the comprehensive application of DT at the social level.


Digital Twins, Smart Home, Environment Monitoring, System Modelling

Cite This Paper

Baichun Bing, Shunhao Yan. The Design of Environment Monitoring Systems Based on Digital Twins in Smart Home. International Journal of Frontiers in Engineering Technology (2022), Vol. 4, Issue 4: 7-13. https://doi.org/10.25236/IJFET.2022.040402.


[1] M. W. Condry and C. B. Nelson, "Using Smart Edge IoT Devices for Safer, Rapid Response With Industry IoT Control Operations," in Proceedings of the IEEE, vol. 104, no. 5, pp. 938-946, May 2016, doi: 10.1109/JPROC.2015.2513672.

[2] R. Muñoz et al., "Integration of IoT, Transport SDN, and Edge/Cloud Computing for Dynamic Distribution of IoT Analytics and Efficient Use of Network Resources," in Journal of Lightwave Technology, vol. 36, no. 7, pp. 1420-1428, 1 April1, 2018, doi: 10.1109/JLT.2018.2800660.

[3] G. Song, F. Ding, W. Zhang and A. Song, "A wireless power outlet system for smart homes," in IEEE Transactions on Consumer Electronics, vol. 54, no. 4, pp. 1688-1691, November 2008, doi: 10.1109/TCE.2008.4711221.

[4] H. Hsu, K. Yu, W. Ouyang and C. Xu, "Constructing a smart home Control System with the Internet of Things", 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP 2018), pp. 128-129, 2018.

[5] R. Rosen, G. V. Wichert, G. Lo and K. D. Bettenhausen, "About the importance of autonomy and Digital Twinss for the future of manufacturing", IFAC-Papersonline, vol. 48, no. 3, pp. 567-572, Aug. 2015.

[6] F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang and F. Sui, "Digital Twins-driven product design manufacturing and service with big data", Int. J. Adv. Manuf. Technol., Mar. 2017.

[7] Q. Qi and F. Tao, "Digital Twins and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison," in IEEE Access, vol. 6, pp. 3585-3593, 2018, doi: 10.1109/ACCESS.2018.2793265.

[8] Yuan Ye, Cao Wei Research on digital twin technology [A]. 2021 (9th) China Water Conservancy Information Technology Forum [C]. 2021

[9] Zhang Lin, Lu Han Digital twin from modeling and simulation [J]. Journal of system simulation, 2021,33 (05): 995-1007 DOI:10.16182/j.issn1004731x. joss. 21-0262.

[10] Liu Ying Exploration and Research on fuzzy control technology in intelligent household appliances [J]. Science and technology innovation guide, 2015, (31): 18-19

[11] Li Liang, Shi Zhigang, Li Guijuan, Zhang Zhijian Modeling and analysis of Smart Home fire early warning system [J]. Computer measurement and control, 2018,26 (06): 138-140 DOI:10.16526/j.cnki. 11-4762/tp. 2018.06.035.

[12] HUSSEINA, ADDAM, ATIEHM, et al. Smart Home design for disabled people based on neural networks [J]. Procedia Computer Science, 2014, 37: 117-126

[13] Bao Xiaoan, Chang Haohao, Xu Hai, Dong Liangliang, Zhang Na Research on prediction model of Smart Home machine learning system based on LSTM [J]. Journal of Zhejiang University of Technology (NATURAL SCIENCE EDITION), 2018,39 (02): 224-231

[14] Sun Shaojing, Chen Changfeng, Li Shigang, Xiao Yanghua, Xu Yingjin, Zhang Taofu, Zhang Zhian, Zhao Zizhong, Zhou Xiao, Zhang Yansong Development and challenge of "algorithm recommendation and artificial intelligence" [J]. Journalism University, 2019 (06): 1-8 + 120

[15] Gao Lingbao, Du Yinxue, Lu Jiangbo, Ma Yongjun, Du Haiping, Hu Xin On machine learning [J]. Foundry equipment and technology, 2021 (06): 41-43 DOI:10.16666/j.cnki. issn1004-6178.2021.06.011.

[16] Zhao Chongwen A review of artificial neural networks [J]. Shanxi electronic technology, 2020 (03): 94-96.