With the increasing focus on sustainable development in society, intelligent domain perception and digital twin technology can be used to evaluate and optimize the design of office furniture. This study analyzed sensor data through machine-learning and data-mining techniques to identify patterns and trends in the office environment. Simultaneously, a digital twin model of office furniture partition space was established to simulate the usage of furniture partition space throughout its full lifecycle. When 50% of nodes fail, the minimum transmission energy mode was significantly better than the maximum greedy forwarding mode in terms of cumulative throughput. The distributed, event-based, unsupervised clustering algorithm successfully reduced communication energy consumption, and the lightweight gradient boosting machine algorithm achieved the best design optimization rate, with an improvement of 0.53%. The ratio of value-added time to non-value-added time increased by 56.3%. The study aimed to provide innovative ideas for the development of intelligent office environments, promote the design of office furniture toward intelligence and sustainability, and improve the adaptability and efficiency of the work environment.

1. [1] C. Flavián, A. Pérez-Rueda, D. Belanche, and L. V. Casaló, “Intention to use analytical artificial intelligence (AI) in services – The effect of technology readiness and awareness,” J. of Service Management, Vol.33, No.2, pp. 293-320, 2022. https://doi.org/10.1108/JOSM-10-2020-0378
2. [2] M. Majid et al., “Applications of wireless sensor networks and Internet of things frameworks in the industry revolution 4.0: A systematic literature review,” Sensors, Vol.22, No.6, Article No.2087, 2022. https://doi.org/10.3390/s22062087
3. [3] N. A. Sepulveda, J. D. Jenkins, A. Edington, D. S. Mallapragada, and R. K. Lester, “The design space for long-duration energy storage in decarbonized power systems,” Nature Energy, Vol.6, No.5, pp. 506-516, 2021. https://doi.org/10.1038/s41560-021-00796-8
4. [4] H. Elayan, M. Aloqaily, and M. Guizani, “Digital twin for intelligent context-aware IoT healthcare systems,” IEEE Internet of Things J., Vol.8, No.23, pp. 16749-16757, 2021. https://doi.org/10.1109/JIOT.2021.3051158
5. [5] T. Fu, S. Liu, and P. Li, “Digital twin-driven smelting process management method for converter steelmaking,” J. of Intelligent Manufacturing, 2024. https://doi.org/10.1007/s10845-024-02366-7
6. [6] H. Liu, Y. P. Tsang, C. K. M. Lee, and C. H. Wu, “UAV trajectory planning via viewpoint resampling for autonomous remote inspection of industrial facilities,” IEEE Trans. on Industrial Informatics, Vol.20, No.5, pp. 7492-7501, 2024. https://doi.org/10.1109/TII.2024.3361019
7. [7] H. Liu, Y. P. Tsang, and C. K. M. Lee, “A cyber-physical social system for autonomous drone trajectory planning in last-mile superchilling delivery,” Transportation Research Part C: Emerging Technologies, Vol.158, Article No.104448, 2024. https://doi.org/10.1016/j.trc.2023.104448
8. [8] H. Liu et al., “Multi-UAV cooperative task planning for border patrol based on hierarchical optimization,” J. of Imaging Science and Technology, Vol.65, No.4, Article No.jist1082, 2021. https://doi.org/10.2352/J.ImagingSci.Technol.2021.65.4.040402
9. [9] M. Sydor, A. Bonenberg, B. Doczekalska, and G. Cofta, “Mycelium-based composites in art, architecture, and interior design: A review,” Polymers, Vol.14, No.1, Article No.145, 2021. https://doi.org/10.3390%2Fpolym14010145
10. [10] Y.-S. Chang, K.-J. Hu, C.-W. Chiang, and A. Lugmayr, “Applying mobile augmented reality (AR) to teach interior design students in layout plans: Evaluation of learning effectiveness based on the ARCS model of learning motivation theory,” Sensors, Vol.20, No.1, Article No.105, 2019. https://doi.org/10.3390/s20010105
11. [11] D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, “Applications of wireless sensor networks: An up-to-date survey,” Applied System Innovation, Vol.3, No.1, Article No.14, 2020. https://doi.org/10.3390/asi3010014
12. [12] H. Landaluce et al., “A review of IoT sensing applications and challenges using RFID and wireless sensor networks,” Sensors, Vol.20, No.9, Article No.2495, 2020. https://doi.org/10.3390/s20092495
13. [13] J. Chen, Z. Shao, and B. Hu, “Generating interior design from text: A new diffusion model-based method for efficient creative design,” Buildings, Vol.13, No.7, Article No.1861, 2023. https://doi.org/10.3390/buildings13071861
14. [14] S. Colenberg and T. Jylhä, “Identifying interior design strategies for healthy workplaces – A literature review,” J. of Corporate Real Estate, Vol.24, No.3, pp. 173-189, 2022. https://doi.org/10.1108/JCRE-12-2020-0068
15. [15] O. I. Khalaf and B. M. Sabbar, “An overview on wireless sensor networks and finding optimal location of nodes,” Periodicals of Engineering and Natural Sciences, Vol.7, No.3, pp. 1096-1101, 2019.
16. [16] P. Gao, J. Li, and S. Liu, “An introduction to key technology in artificial intelligence and big data driven e-learning and e-education,” Mobile Networks and Applications, Vol.26, No.5, pp. 2123-2126, 2021. https://doi.org/10.1007/s11036-021-01777-7
17. [17] R. S. Dornelas and D. A. Lima, “Correlation filters in machine learning algorithms to select demographic and individual features for autism spectrum disorder diagnosis,” J. of Data Science and Intelligent Systems, Vol.1, No.2, pp. 105-127, 2023. https://doi.org/10.47852/bonviewJDSIS32021027
18. [18] K. Zhang, Y. Zhu, S. Maharjan, and Y. Zhang, “Edge intelligence and blockchain empowered 5G beyond for the industrial Internet of things,” IEEE Network, Vol.33, No.5, pp. 12-19, 2019. https://doi.org/10.1109/MNET.001.1800526
19. [19] M. Dibaei et al., “Attacks and defenses on intelligent connected vehicles: A survey,” Digital Communications and Networks, Vol.6, No.4, pp. 399-421, 2020. https://doi.org/10.1016/j.dcan.2020.04.007
20. [20] K. Guo, Z. Yang, C.-H. Yu, and M. J. Buehler, “Artificial intelligence and machine learning in design of mechanical materials,” Materials Horizons, Vol.8, No.4, pp. 1153-1172, 2021. https://doi.org/10.1039/D0MH01451F