UAV-enabled emergency medicine delivery engineering

Interdisciplinary Areas: Internet of Things and Cyber Physical Systems, Engineering and Healthcare/Medicine/Biology, Data/Information/Computation, Smart City, Infrastructure, Transportation

Project Description

While unmanned aerial vehicles (UAVs, or drones) have long been utilized in military applications, they are now generating considerable attention in the civilian sector. This research takes advantage of the recent development of fog computing architecture that moves storage, computing, control, and communication closer to the network edge, and addresses the challenges in its application that utilizes drones in delivering emergency medical services. In this research, we propose to customize real-time modeling (e.g., scheduling in a networked dynamic system of UAVs) and optimization algorithms under computing hardware limitations and physical constraints on a UAV system platform. In addition, we propose to advance our ability of solving large data-driven stochastic dynamic optimization problems arising from multi-mode emergency logistic network design and operations optimization under periodic update on the service demand forecast through integration of emergency medical services (EMS) data and drone-collected data (biomedical sensing, audio/video signals, etc.). Ultimately, the postdoctoral fellow is expected to participate in the development of a cyber-physical system framework to facilitate the modeling, control, and optimization of drone operations and collaborations in emergency medical services such as medical emergency response for cardiac arrests, hypoglycemia events, and traumatic brain injuries.

Start Date

May 1, 2019

Postdoc Qualifications

We are seeking a highly qualified individual with expertise in system engineering applied to healthcare, and in particular, to point-of-care and mHealth healthcare technologies. Areas of emphasis include stochastic programming, integer programming, predictive analytics, multi-agent control, distributed computing, as well as IoT/remote monitoring/point-of-care analytics. 

Candidates must hold a Ph.D. in biomedical engineering, computer engineering, computer science, electrical engineering, industrial engineering, aerospace engineering, mechanical engineering, statistics, or a related field.

Co-advisors

Nan Kong, nkong@purdue.edu, BME

Dengfeng Sun, dsun@purdue.edu, AAE

References

1. A. Claesson, Emergency Drone System Displays Effective EMS and Rescue Applications. Journal of Emergency Medical Services. Jun 1, 2018, Available at https://www.jems.com/operations.html.

2. M. Chiang, S. Ha, C. L. I., F. Risso, T. Zhang, "Clarifying fog computing and networking: 10 questions and answers", IEEE Commun. Mag., vol. 55, no. 4, pp. 18-20, Apr. 2017.

3. J. J. Boutilier, S. C. Brooks, A. Jammohamed, A. Byers, J. E. Buick, C. Zhan, A. P. Schoelling, S. Cheskes, L. J. Morrison, T. C. Y. Chan, Rescu Epistry Investigators, “Optimizing a drone network to deliver automated external defibrillators”, Circulation, vol. 135, no. 25, pp. 2454 – 2465, Mar. 2017.

4. K. Sudtachat, M. E. Mayorga, L. A. McLay, “A nested-compliance table policy for emergency medical service systems under relocation”, International Journal of Management Science, vol. 58, pp. 154 – 168. Jan. 2016.

5. J. Chen, L. Chen, D. Sun, “Air Traffic Flow Management under Uncertainty Using Chance-Constrained Optimization”, Transportation Research Part B: Methodological, vol.102, pp.124-141, 2017. doi: 10.1016/j.trb.2017.05.014.