Enhancing Rescue Operations in Dubai Police’s Transport and Rescue Department through IoT and AI Integration
DOI:
https://doi.org/10.47604/jppa.3333Keywords:
IoT, AI, Rescue Operation, Dubai Police, SARAbstract
Purpose: This research proposes how all the space, the Internet of Things, and Artificial Intelligence technologies can be better used in the Transport and Rescue Department of the Dubai Police. With the rapid evolution in smart technologies, IoT and AI have much scope to refine rescue operations regarding time management, accuracy, resources, and overall processes. Since the requirements in rescue missions are often complex, including environmental change and the need for urgency, the role of IoT and AI is extremely important in decision-making and successful operation. This study investigates how to employ these technologies within Dubai Police's existing rescue framework, which has gaps such as slow response or response delay and inefficient resource distribution. It aims to explore the potential benefits and challenges that the use of these technologies entails while drawing lessons from global best practices on emergency response more generally.
Methodology: Using qualitative and quantitative research methods, including interviews, surveys, observation, global case studies, and reviews of existing literature, the research will make recommendations on how these technologies can be effectively used to enhance the success of rescue missions. By fully embracing the concept of Dubai being a smart city, this research will not only upgrade the local emergency response systems, but it will also create a model for the other regions that want to convert to advanced technologies in rescue operations. At the heart of the research is a goal to reach more efficient, faster, and safer rescue operations that, in turn, ensure public safety in Dubai.
Findings: Research results show that the Dubai Police have already started using a variety of technology to ensure smart operations (e.g., SAS analytics and VR training), but IoT and AI integration are hampered by infrastructure, technology training gaps, and interagency coordination issues. Rescuers recognize that AI and IoT can be utilized to help in enhancing situational awareness and speeding the response, however, they also flag issues about cybersecurity and the system's compatibility.
Unique Contribution to Theory, Practice and Policy: The implication of the research is two-fold. First, it gives specific, practical recommendations on what to do to enhance Dubai's emergency response system. Second, it presents a scalable model for cities that are trying to equip themselves with smart, technology-driven rescue systems.
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References
Ahmetoglu, S., Che Cob, Z., & Ali, N. A. (2022). A systematic review of Internet of Things adoption in organizations: Taxonomy, benefits, challenges and critical factors. Applied Sciences, 12(9), 4117. https://doi.org/10.3390/app12094117
Alahi, M. E. E., Sukkuea, A., Tina, F. W., Nag, A., Kurdthongmee, W., Suwannarat, K., & Mukhopadhyay, S. C. (2023). Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23(11), 5206. https://doi.org/10.3390/s23115206
Ali, A. (2023, December 8). Dubai search-and-rescue teams responded to 3,520 incidents so far this year. Gulf News. https://gulfnews.com/uae/dubai-search-and-rescue-teams-responded-to-3520-incidents-so-far-this-year-1.99789364
Alshamsi, O. M. A. (2022). Framework for embedding industry 4.0 in UAE emergency management. Sheffield Hallam University (United Kingdom). https://doi.org/10.7190/shu-thesis-00445
Bajwa, A. (2025). AI-based emergency response systems: A systematic literature review on smart infrastructure safety. Available at SSRN 5171521. https://doi.org/10.2139/ssrn.5171521
Bakıcı, T., Almirall, E., & Wareham, J. (2013). A smart city initiative: the case of Barcelona. Journal of the Knowledge Economy, 4, 135-148. https://doi.org/10.1007/s13132-012-0084-9
Balduzzi, M., Pasta, A., & Wilhoit, K. (2014, December). A security evaluation of AIS automated identification system. In Proceedings of the 30th Annual Computer Security Applications Conference (pp. 436-445). https://doi.org/10.1145/2664243.2664257
Cabinet Office. (2025). Resilient communications. GOV.UK. https://www.gov.uk/guidance/resilient-communications
Crawl, D., Block, J., Lin, K., & Altintas, I. (2017). FireMap: A dynamic data-driven predictive wildfire modelling and visualization environment. Procedia Computer Science, 108, 2230-2239. https://doi.org/10.1016/j.procs.2017.05.174
Desk, W. (2024, February 14). Video: Dubai Police rescue 2 elderly people from 5 vehicles almost swept away by floods. Khaleej Times. https://www.khaleejtimes.com/uae/dubai-police-rescue-2-elderly-people-from-5-vehicles-almost-swept-away-by-floods
Farsath, K. R., Jitha, K., Marwan, V. M., Jouhar, A. M. A., Farseen, K. M., & Musrifa, K. A. (2024, March). AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue Operations. In 2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT) (pp. 1-10). IEEE. https://doi.org/10.1109/icitiit61487.2024.10580372
Freire, D. V. C. (2023). Mission-Critical Communications from LMR to 5G: A Technology Assessment Approach for Smart City Scenarios (Doctoral dissertation, Universidade NOVA de Lisboa (Portugal)). https://doi.org/10.13140/RG.2.2.35913.56167
Grieco, L. A., Rizzo, A., Colucci, S., Sicari, S., Piro, G., Di Paola, D., & Boggia, G. (2014). IoT-aided robotics applications: Technological implications, target domains and open issues. Computer Communications, 54, 32-47. https://doi.org/10.1016/j.comcom.2014.07.013
Kumaran, S., Raj, V. A., Sangeetha, J. & Raman, V. R. (2023). IoT-based Autonomous Search and Rescue Drone for Precision Firefighting and Disaster Management. International Journal of Advanced Computer Science & Applications, 14(11). https://doi.org/10.14569/ijacsa.2023.0141145
Mahmood, M. R., Matin, M. A., Sarigiannidis, P., & Goudos, S. K. (2022). A comprehensive review on artificial intelligence/machine learning algorithms for empowering the future IoT toward 6G era. IEEE Access, 10, 87535-87562. https://doi.org/10.1109/access.2022.3199689
Militano, L., Arteaga, A., Toffetti, G., & Mitton, N. (2023). The cloud-to-edge-to-IoT continuum as an enabler for search and rescue operations. Future Internet, 15(2), 55. https://doi.org/10.3390/fi15020055
Nasar, W., Da Silva Torres, R., Gundersen, O. E., & Karlsen, A. T. (2023). The use of decision support in search and rescue: A systematic literature review. ISPRS International Journal of Geo-Information, 12(5), 182. https://doi.org/10.3390/ijgi12050182
OPSI. (2024). Virtual Singapore – Singapore’s virtual twin. Observatory of Public Sector Innovation -. https://oecd-opsi.org/innovations/virtual-twin-singapore/
Pathik, N., Gupta, R. K., Sahu, Y., Sharma, A., Masud, M., & Baz, M. (2022). Ai enabled accident detection and alert system using IoT and deep learning for smart cities. Sustainability, 14(13), 7701. https://doi.org/10.3390/su14137701
Păvăloaia, V. D., & Necula, S. C. (2023). Artificial intelligence as a disruptive technology—a systematic literature review. Electronics, 12(5), 1102. https://doi.org/10.3390/electronics12051102
Qiu, T., Zheng, K., Han, M., Chen, C. P., & Xu, M. (2018). A data-emergency-aware scheduling scheme for Internet of Things in smart cities. IEEE Transactions on Industrial Informatics, 14(5), 2042-2051. https://doi.org/10.1109/tii.2017.2763971
Qiu, T., Zheng, K., Song, H., Han, M., & Kantarci, B. (2017). A local-optimization emergency scheduling scheme with self-recovery for a smart grid. IEEE Transactions on Industrial Informatics, 13(6), 3195-3205. https://doi.org/10.1109/tii.2017.2715844
Risdiana, D. M., & Susanto, T. D. (2019). The safe city: Conceptual model development-A systematic literature review. Procedia Computer Science, 161, 291-299. https://doi.org/10.1016/j.procs.2019.11.126
Salamati, K. (n.d.). Smart cities with AI for flooding management a case study of Jakarta. Ieee.org. https://smartcities.ieee.org/newsletter/june-july-2024/smart-cities-with-ai-for-flooding-management-a-case-study-of-jakarta
Santos, A. S., Goncales, I., Silva, A., Neves, R., Teixeira, I., Barbosa, E., ... & Yoshida, O. (2024). Smart resilience through IoT‐enabled natural disaster management: A COVID‐19 response in São Paulo state. IET Smart Cities, 6(3), 211-224. https://doi.org/10.1049/smc2.12082
Sebastian, A. (2025). Soft Robotics for Search and Rescue: Advancements, Challenges, and Future Directions. arXiv preprint arXiv:2502.12373. https://doi.org/10.48550/arXiv.2502.12373
Wu, F., Redouté, J. M., & Yuce, M. R. (2018). We-safe: A self-powered wearable iot sensor network for safety applications based on lora. IEEE Access, 6, 40846-40853. https://doi.org/10.1109/access.2018.2859383
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