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From Smart Home to Smart warehouse: A Comprehensive DIY case study on IoT-Enabled Digital Twins

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Author Affiliations

  • 1Department of Mechanical Engineering, Marwadi University, Rajkot, India and Department of Production Engineering, Shantilal Shah Engineering College, Bhavnagar, India
  • 2Department of Mechanical Engineering, Marwadi University, Rajkot, India

Res. J. Engineering Sci., Volume 15, Issue (1), Pages 30-36, January,26 (2026)

Abstract

The rapid growth of the Internet of Things (IoT) and digital twin (DT) technologies has transformed domestic and industrial environments. Although proprietary smart home solutions and traditional warehouse management systems (WMS) exist, they are often expensive and locked within vendor ecosystems. This study presents a mechanical engineering-oriented study of IoT-enabled automation, treating the home as a micro-digital twin laboratory and extending the same principles to warehouse automation. By leveraging advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), the smart home concept has been extended to the warehousing sector. Conventional appliances and systems were retrofitted with actuators, sensors, and IoT modules to enable automation using Alexa, Google Home, Siri, and Bixby. The key implementations include(1) a cleaning robot with SLAM-based mapping, (2) an automated plant-watering system using soil moisture sensing and fluid actuation, and (3) HVAC load control with PID regulation. Mathematical models (kinematics, fluid flow, and thermal loads), block diagrams, and simulations were used to validate the behavior of the system. Cost–benefit analysis shows that DIY retrofits achieve ~85% functionality at ~30–40% of the cost of commercial systems. Finally, analogies to warehouse digital twins are drawn, demonstrating how cleaning robots parallel AGVs, how watering systems are scaled to cold storage climate control, and how smart locks align with industrial access systems. An analogy between home and warehouse automation was then established: cleaning robots were scaled to Automated Guided Vehicles (AGVs), watering loops were mapped to warehouse climate control, and smart locks were extended to industrial access management. This demonstrates that smart homes can serve as micro digital twin laboratories for mechanical engineers, enabling the prototyping of control systems before scaling them to industrial cyber–physical systems. The findings underline the contributions of mechanical engineering in kinematics, fluid dynamics, thermodynamics, control, and system integration, which drive the transformation of Industry 4.0.

References

  1. Filipescu, A., Simion, G., Ionescu, D., &Filipescu, A. (2024)., IoT-Cloud, VPN, and Digital Twin-Based Remote Monitoring and Control of a Multifunctional Robotic Cell in the Context of AI, Industry, and Education 4.0 and 5.0., Sensors (Basel, Switzerland), 24(23), 7451. https://doi.org/10.3390/s24237451
  2. Singh, R. R., Bhatti, G., Alsaif, F., Vairavasundaram, I., & Kalel, D. (2023)., Building a Digital Twin Powered Intelligent Predictive Maintenance System for Industrial AC Machines., Machines, 11(8), 796. https://doi.org/10. 3390/machines11080796
  3. Chen, Y., Tsai, Y., Karkaria, V., & Chen, W. (2025)., Uncertainty-Aware Digital Twins: Robust Model Predictive Control Using Time-Series Deep Quantile Learning., Journal of Mechanical Design, 148(2). https://doi.org/10.1115/1.4069104
  4. Rasheed, A., Kvamsdal, T., & San, O. (2020)., Digital Twin: Values, Challenges and Enablers From a Modeling Perspective., IEEE Access, 8, 21980–22012. https://doi.org/10.1109/access.2020.2970143
  5. Maheshwari, P., Kamble, S., Kumar, S., Belhadi, A., & Gupta, S. (2023)., Digital twin-based warehouse management system: a theoretical toolbox for future research and applications., The International Journal of Logistics Management, 35(4), 1073–1106. https://doi.org/10.1108/ijlm-01-2023-0030
  6. Corallo, A., Lezzi, M., Del Vecchio, V. D., & Morciano, P. (2021)., Shop Floor Digital Twin in Smart Manufacturing: A Systematic Literature Review., Sustainability, 13(23), 12987. https://doi.org/10.3390/su132312987
  7. Tural, E., Lu, D., & Austin Cole, D. (2021)., Safely and Actively Aging in Place: Older Adults’ Attitudes and Intentions Toward Smart Home Technologies., Gerontology and Geriatric Medicine, 7(4), 233372142110173. https://doi.org/10.1177/23337214211 017340
  8. Hargreaves, T., Wilson, C., & Hauxwell-Baldwin, R. (2017)., Learning to live in a smart home., Building Research & Information, 46(1), 127–139. https://doi.org/10.1080/09613218.2017.1286882
  9. Govindraj, V., Sathiyanarayanan, M., & Abubakar, B. (2017)., Customary homes to smart homes using Internet of Things (IoT) and mobile application., In 2017 International Conference on Smart Technologies For Smart Nation (SmartTechCon) (pp. 1059-1063).
  10. Venkatraman, S., Overmars, A., & Thong, M. (2021)., Smart Home Automation—Use Cases of a Secure and Integrated Voice-Control System., Systems, 9(4), 77. https://doi.org/10.3390/systems9040077
  11. Gomes, B. D. T. P., Ríos, L. E. T., Da Silva E Silva, F. J., Muniz, L. C. M., & Endler, M. (2016)., A comprehensive and scalable middleware for Ambient Assisted Living based on cloud computing and Internet of Things., Concurrency and Computation: Practice and Experience, 29(11), e4043. https://doi.org/10.1002/cpe.404 3
  12. Lobaccaro, G., Löfström, E., & Carlucci, S. (2016)., A Review of Systems and Technologies for Smart Homes and Smart Grids., Energies, 9(5), 348. https://doi.org/10.3390/ en9050348
  13. Mikołajewska, E., Mikołajczyk, T., Paczkowski, T., &Mikołajewski, D. (2025)., Generative AI in AI-Based Digital Twins for Fault Diagnosis for Predictive Maintenance in Industry 4.0/5.0., Applied Sciences, 15(6), 3166. https://doi.org/10.3390/app15063166
  14. Han, W., Zhang, Z., Mei, X., Zhang, K., Liu, B., Sun, Z., & Xu, J. (2022)., Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems., Applied Sciences, 12(7), 3354. https://doi.org/ 10.3390/app12073354
  15. Wang, Q., Li, J., Yang, Z., Li, P., Xia, G., & Yang, L. (2022)., Distributed Multi-Mobile Robot Path Planning and Obstacle Avoidance Based on ACO–DWA in Unknown Complex Terrain., Electronics, 11(14), 2144. https://doi.org/10.3390/electronics11142144
  16. Liu, X., Shang, G., Hu, X., Gong, G., & Zhu, H. (2024)., Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization., Electronics, 13(5), 818. https://doi.org/10. 3390/electronics13050818
  17. Seraj, M., Parvez, M., Khan, O., & Yahya, Z. (2024)., Optimizing smart building energy management systems through industry 4.0: A response surface methodology approach., Green Technologies and Sustainability, 2(2), 100079. https://doi.org/10.1016/j.grets.2024.100079
  18. Ding, J., Cao, S.-J., & Yu, C. W. (2020)., HVAC systems for environmental control to minimize the COVID-19 infection., Indoor and Built Environment, 29(9), 1195–1201. https://doi.org/10.1177/1420326x20951968
  19. Mansour, M., Ahmed, A. I., Elbaz, A., Herencsar, N., Gamal, A., Soltan, A., & Said, L. A. (2023)., Internet of Things: A Comprehensive Overview on Protocols, Architectures, Technologies, Simulation Tools, and Future Directions., Energies, 16(8), 3465. https://doi.org/10.3390/ en16083465
  20. Shaikh, M. S., Choudhary, S. M., Ali, S. I., Ponnusamy, S., Chandrawat, U. B. S., Sheikh, A. G., & Khan, R. A. H. (2024)., Harnessing Logistic Industries and Warehouses With Autonomous Carebot for Security and Protection., pp. 239–257. Igi Global. https://doi.org/10.4018/979-8-3693-3234-4.ch017.
  21. Lee, C. K. M., Park, T., Chung, S. Y., & Ip, C. M. (2019)., A Bluetooth Location-based Indoor Positioning System for Asset Tracking in Warehouse., 1408–1412. https://doi.org/10.1109/ieem44572.2019.8978639
  22. Frankó, A., Vida, G., & Varga, P. (2020)., Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0., Sensors (Basel, Switzerland), 20(13), 3709. https://doi.org/10.3390/s20133709
  23. Zeb, S., Mahmood, A., Hassan, S. A., Piran, M. J., Gidlund, M., &Guizani, M. (2022)., Industrial digital twins at the nexus of NextG wireless networks and computational intelligence: A survey., Journal of Network and Computer Applications, 200, 103309. https://doi.org/10.1016/j.jnca. 2021.103309
  24. Karaarslan, E., & Babiker, M. (2021)., Digital Twin Security Threats and Countermeasures: An Introduction., 174, 7–11. https://doi.org/10.1109/iscturkey 53027.2021.9654360
  25. Li, Z., Zhou, X., Tian, Z., Wang, W. M., Huang, G., & Huang, S. (2018)., An ontology-based product design framework for manufacturability verification and knowledge reuse., The International Journal of Advanced Manufacturing Technology, 99(9–12), 2121–2135. https://doi.org/10.1007/s00170-018-2099-2
  26. Freepik. (2025)., Robot vacuum cleaner photograph., https://www.freepik.com
  27. Freepik. (2025)., Warehouse robot photograph., https://www.freepik.com
  28. Publicly available pricing information from IoT hardware vendors, automation suppliers, and digital twin software providers (2025)., undefined, undefined