Descriptive Analysis Dashboard for Health and Safety Monitoring in Smart Buildings Using IoT Sensor Data

Safety and Health in smart buildings especially in terms of the air monitoring, can be enhanced through IoT sensor data that support effective safety measures and environmental monitoring. However, the existing system often lacks the ability to analyze the historical IoT sensor data and are limited by the absence of descriptive visual analytics. This gap prevents the ability to identify long-term safety trends and patterns in smart buildings. Therefore, this study aims to study the environmental factors and safety parameters in health and safety monitoring of smart buildings using IoT sensor data. It involves collecting relevant data and designing a descriptive analysis dashboard using Microsoft Power BI to support better decision-making. Using Microsoft Power BI, IoT sensor data such as CO₂ levels, temperature, humidity, motion detection (PIR), and light intensity are analyzed to identify patterns and visualize safety related trends. In addition, this study aims to identify and recommend appropriate mitigation strategies based on the levels of health and safety indicators displayed in the dashboard for actionable decision-making. The expected results from the proposed interactive dashboard will provide valuable insights to help safety officers and facility managers detect risks, improve air quality, optimize lighting, and enhance ventilation

Environmental Studies

1. Almasi, S., Bahaadinbeigy, K., Ahmadi, H., Sohrabei, S., & Rabiei, R. (2023). Usability evaluation of dashboards: A systematic literature review of tools. BioMed Research International, 2023, Article ID 9990933. https://doi.org/10.1155/2023/9990933 [Google Scholar] [Crossref]

2. ASHRAE. (2019). ANSI/ASHRAE Standard 62.1-2019: Ventilation for acceptable indoor air quality. American Society of Heating, Refrigerating and Air-Conditioning Engineers. [Google Scholar] [Crossref]

3. Cheah, C. Q. (2022). A visualized analysis on student performance by using cloud business intelligence (Bachelor’s thesis, Universiti Tunku Abdul Rahman). Universiti Tunku Abdul Ra11111hman Institutional Repository. [Google Scholar] [Crossref]

4. Chen, L., Chen, Y., & Sun, Y. (2020). Indoor air quality monitoring systems based on Internet of Things: A systematic review. Journal of Sensors, 2020, Article ID 8862046. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400061/ [Google Scholar] [Crossref]

5. Chourasia, D., Patel, A., & Yadav, D. (2023). IoT-based indoor air quality monitoring system. ResearchGate. https://www.researchgate.net/publication/389632095_IOT-Based_Indoor_Air_Quality_Monitoring_System [Google Scholar] [Crossref]

6. Cano-Ortega, A., Cano-Ortega, R., & Cano-Ortega, I. (2023). Implementation of an Internet of Things architecture to monitor indoor air quality. Sensors, 23(6), 1683. https://www.mdpi.com/1424-8220/23/6/1683 [Google Scholar] [Crossref]

7. Deloitte. (2023). The future of smart building data analytics: Trends, challenges, and opportunities. Deloitte Insights. https://www2.deloitte.com/content/dam/insights/articles/us175897_tech-trends-2023/DI_tech-trends-2023.pdf [Google Scholar] [Crossref]

8. Ferreira, A. J., Marques, G., Pitarma, R., & Garcia, N. M. (2024). Low-cost IoT-based indoor air quality monitoring. International Journal of Smart Technology and Learning, 6(1), 44–59. https://www.tandfonline.com/doi/full/10.1080/24751448.2024.2405403 [Google Scholar] [Crossref]

9. Ghahramani, A., Arroyo, L., & Klein, L. (2022). Smart buildings and indoor environmental quality: A review of sensor applications for health and safety. Building and Environment, 221, 109325. https://doi.org/10.1016/j.buildenv.2022.109325 [Google Scholar] [Crossref]

10. Ghahramani, M., Becerik-Gerber, B., & Kichkaylo, T. (2022). Smart building health and safety monitoring using IoT and machine learning: A review. Automation in Construction, 135, 104127. https://doi.org/10.1016/j.autcon.2022.104127 [Google Scholar] [Crossref]

11. Ha, J., Lee, S., & Kim, H. (2020). Sensing data fusion for enhanced indoor air quality monitoring. https://arxiv.org/abs/2001.01976 [Google Scholar] [Crossref]

12. Hong, D., Gu, Q., & Whitehouse, K. (2017). High-dimensional time series clustering via cross-predictability. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). https://arxiv.org/abs/1703.03952 [Google Scholar] [Crossref]

13. Henson, R. (2020). Learning Tableau: A Data Visualization Tool. Journal of Economic Education, 51(4), 433-440. https://doi.org/10.1080/00220485.2020.1804503 [Google Scholar] [Crossref]

14. Honeywell. (n.d.). Healthy buildings dashboard. Honeywell Building Technologies. Retrieved June 20, 2025, from https://buildings.honeywell.com [Google Scholar] [Crossref]

15. Iqbal, S., Noor, M., & Ahmad, R. (2022). Evaluating Power BI for IoT data analytics in built environments. Journal of Applied Data Science, 3(4), 201–210. https://doi.org/10.1007/s42452-022-04567-8 [Google Scholar] [Crossref]

16. Kumar, A., & Lee, J. (2022). IoT—A promising solution to energy management in smart buildings: A systematic review, applications, barriers, and future scope. Buildings, 14(11), 3446. https://doi.org/10.3390/buildings14113446 [Google Scholar] [Crossref]

17. Kabir, M. A., & Ahmed, M. R. (2024). Python for data analytics: A systematic literature review of tools, techniques, and applications. Academic Journal on Science, Technology, Engineering & Mathematics Education. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5051680 [Google Scholar] [Crossref]

18. Kumar, S. S., Chandra, R., & Agarwal, S. (2023). A real-time approach for smart building operations prediction using rule-based complex event processing and SPARQL query. https://arxiv.org/abs/2309.10822 [Google Scholar] [Crossref]

19. Li, H., & Chen, W. (2023). Development of an IoT-based indoor air quality monitoring system for smart buildings. Journal of Building Engineering, 57, 104837. https://doi.org/10.1016/j.jobe.2023.104837 [Google Scholar] [Crossref]

20. Mohammed, A. K., & Ansari, M. A. (2024). The impact and limitations of AI in Power BI: A review. International Journal of Multidisciplinary Research and Publications (IJMRAP), 7(7), 23–27. https://ijmrap.com/wp-content/uploads/2024/12/IJMRAP-V7N6P136Y24.pdf [Google Scholar] [Crossref]

21. Singh, A., Tan, Y. C., & Bakar, H. (2022). Application of IoT sensors in monitoring indoor environments for health and safety. Sensors and Actuators A: Physical, 341, 113602. https://doi.org/10.1016/j.sna.2022.113602 [Google Scholar] [Crossref]

22. Schneider Electric. (n.d.). EcoStruxure™ Building Operation. Retrieved June 20, 2025, from https://www.se.com/ww/en/work/products/buildings/ecostruxure-building/ [Google Scholar] [Crossref]

23. World Health Organization. (2021). WHO housing and health guidelines. https://www.who.int/publications/i/item/9789241550376 [Google Scholar] [Crossref]

24. Zhou, Y., Wang, S., & Zhang, X. (2022). Real-time indoor environmental quality monitoring and feedback system for smart buildings. Energy and Buildings, 258, 111831. https://doi.org/10.1016/j.enbuild.2022.111831 [Google Scholar] [Crossref]

25. Zhao, Y., Zhang, J., & Lee, H. (2022). Descriptive analytics and dashboards in smart city management. Smart Cities, 5(3), 320–338. https://doi.org/10.3390/smartcities5030018 [Google Scholar] [Crossref]

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