Document Open Access Logo

A Multi-UAV Router and Scheduler for Executing Spatially Scattered Real-Time Tasks

Authors Sreyashi Mukherjee , Sachin Yadav , Yedla Anil Kumar , Arnab Sarkar

PDF HTML 5

Files

Thumbnail PDF
PDF
LIPIcs.ECRTS.2025.4.pdf
  • Filesize: 1.59 MB
  • 25 pages
HTML5 Logo HTML (experimental)
LIPIcs.ECRTS.2025.4.html

Document Identifiers

Subject Classification

ACM Subject Classification
  • Computer systems organization → Real-time system specification
  • Computer systems organization → Real-time system architecture
  • Computer systems organization → Robotics
Keywords
  • UAV Scheduling
  • Task Allocation
  • Optimization
  • Execution Time

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

Cyber-Physical Systems (CPSs) operating in remote or field scenarios often face limited local processing capacity, necessitating complex real-time monitoring and control via remote processing through mobile edge networks, satellite systems, or UAVs. With recent advancements, UAVs are increasingly being favored for such applications, particularly in isolated areas beyond edge or satellite network coverage. This paper presents a unified UAV scheduling and routing framework for executing geographically distributed real-time CPS tasks under both periodic and aperiodic arrival models. We address the challenge of minimizing the number of UAVs required while ensuring strict adherence to task deadlines across diverse temporal and spatial settings. At first, we propose an efficient heuristic strategy called UAV Scheduling and Routing Algorithm for Real-time Tasks - Periodic Arrivals (USRART-P), which decomposes applications into task instances and sequentially creates per-UAV routes and schedules within a hyperperiod, maximizing the number of task instances each UAV can cover while meeting deadlines. Adapting to this framework, we develop two additional variants to handle aperiodic CPS tasks: USRART-SA for Synchronous Aperiodic Arrivals (common arrival time, distinct deadlines) and USRART-AA for Asynchronous Aperiodic Arrivals (distinct but known arrival times and deadlines). For the case of periodic tasks, we frame the problem as a constraint optimization formulation which aims to minimize the number of UAVs that are required to generate static hyperperiodic travel routes with task execution schedules for all UAVs, and discuss how the formulation can be adapted for aperiodic tasks. Solution to this formulation using standard off-the-shelf solvers achieves optimality but incurs high computational overheads. Through extensive simulations, we show that USRART exhibits high performance across diverse operational scenarios, varying task distributions, execution demands, and spatial layouts. The results emphasize USRART’s flexibility and effectiveness in real-world UAV-based CPS scenarios, especially in environments with limited resources and infrastructure.

Cite As Get BibTex

Sreyashi Mukherjee, Sachin Yadav, Yedla Anil Kumar, and Arnab Sarkar. A Multi-UAV Router and Scheduler for Executing Spatially Scattered Real-Time Tasks. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 4:1-4:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ECRTS.2025.4

Author Details

Sreyashi Mukherjee
  • ATDC, IIT Kharagpur, India
Sachin Yadav
  • Civil Engineering, IIT Kharagpur, India
Yedla Anil Kumar
  • Chemical Engineering, IIT Kharagpur, India
Arnab Sarkar
  • ATDC, IIT Kharagpur, India

References

  1. Mansoor Akhtar, Muhammad Raffeh, Fakhar ul Zaman, Ali Ramzan, Sohaib Aslam, and Faisal Usman. Development of congestion level based dynamic traffic management system using iot. In 2020 international conference on electrical, communication, and computer engineering (ICECCE), pages 1-6. IEEE, 2020. Google Scholar
  2. Oluwatosin Ahmed Amodu, Rosdiadee Nordin, Chedia Jarray, Umar Ali Bukar, Raja Azlina Raja Mahmood, and Mohamed Othman. A survey on the design aspects and opportunities in age-aware UAV-aided data collection for sensor networks and internet of things applications. Drones, 7(4):260, 2023. Google Scholar
  3. David L. Applegate, Robert E. Bixby, Václav Chvátal, and William J. Cook. The Traveling Salesman Problem: A Computational Study. Princeton University Press, 2007. Google Scholar
  4. Muhammet Fatih Aslan, Akif Durdu, Kadir Sabanci, Ewa Ropelewska, and Seyfettin Sinan Gültekin. A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses. Applied Sciences, 12(3):1047, 2022. Google Scholar
  5. Martin Aubard, Sergio Quijano, Olaya Álvarez-Tuñón, László Antal, Maria Costa, and Yury Brodskiy. Mission planning and safety assessment for pipeline inspection using autonomous underwater vehicles: A framework based on behavior trees. arXiv preprint arXiv:2402.04045, 2024. URL: https://doi.org/10.48550/arXiv.2402.04045.
  6. Murat Bakirci. Smart city air quality management through leveraging drones for precision monitoring. Sustainable Cities and Society, 106:105390, 2024. Google Scholar
  7. Cristian Cataldo-Díaz, Rodrigo Linfati, and John Willmer Escobar. Mathematical models for the electric vehicle routing problem with time windows considering different aspects of the charging process. Annals of Operations Research, 2023. URL: https://doi.org/10.1007/s10479-023-05713-z.
  8. Long Chen, Guangrui Liu, Xia Zhu, and Xin Li. A heuristic routing algorithm for heterogeneous UAVs in time-constrained mec systems. In IEEE International Conference on Communications (ICC), 2024. Google Scholar
  9. Jean-François Cordeau and Gilbert Laporte. Models and algorithms for the dynamic dial-a-ride problem. Transportation Science, 43(2):86-99, 2009. Google Scholar
  10. Jiong Dong, Kaoru Ota, and Mianxiong Dong. UAV-based real-time survivor detection system in post-disaster search and rescue operations. IEEE Journal on Miniaturization for Air and Space Systems, 2(4):209-219, 2021. Google Scholar
  11. R Ebrahim, MM Bruwer, and SJ Andersen. Small-city traffic management using unmanned aerial vehicles (UAVs). In Proceedings of the Southern African Transport Conference. Southern African Transport Conference 2021, 2021. Google Scholar
  12. Boris Galkin, Jacek Kibilda, and Luiz A. DaSilva. Coverage analysis for low-altitude uav networks in urban environments. In GLOBECOM 2017 - 2017 IEEE Global Communications Conference, pages 1-6, 2017. URL: https://doi.org/10.1109/GLOCOM.2017.8254658.
  13. IBM. Ibm ilog cplex optimization studio. Online Resource, 2024. Google Scholar
  14. Ya-Hui Jia, Yi Mei, and Mengjie Zhang. A bilevel ant colony optimization algorithm for capacitated electric vehicle routing problem. IEEE transactions on cybernetics, 52(10):10855-10868, 2021. URL: https://doi.org/10.1109/TCYB.2021.3069942.
  15. Merve Keskin and Bülent Çatay. Partial recharge strategies for the electric vehicle routing problem with time windows. Transportation research part C: emerging technologies, 65:111-127, 2016. Google Scholar
  16. Navid Ali Khan, NZ Jhanjhi, Sarfraz Nawaz Brohi, Raja Sher Afgun Usmani, and Anand Nayyar. Smart traffic monitoring system using unmanned aerial vehicles (UAVs). Computer Communications, 157:434-443, 2020. URL: https://doi.org/10.1016/J.COMCOM.2020.04.049.
  17. Jay Lee, Behrad Bagheri, and Hung-An Kao. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3:18-23, 2015. Google Scholar
  18. M. Anwar Ma’sum, M. Kholid Arrofi, Grafika Jati, Futuhal Arifin, M. Nanda Kurniawan, Petrus Mursanto, and Wisnu Jatmiko. Simulation of intelligent unmanned aerial vehicle (UAV) for military surveillance. In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pages 161-166, 2013. Google Scholar
  19. Alejandro Montoya, Christelle Guéret, Jorge E. Mendoza, and Juan G. Villegas. The electric vehicle routing problem with nonlinear charging function. Transportation Research Part B: Methodological, 103:87-110, 2017. URL: https://doi.org/10.1016/j.trb.2017.02.004.
  20. Robin R. Murphy, Jennifer Kravitz, Scott Stover, and Rahmat A. Shoureshi. Mobile robots in mine rescue and recovery. In Proceedings of the IEEE 2008 International Conference on Technologies for Homeland Security, pages 294-299, 2008. Google Scholar
  21. Pawel Olejnik, Martin Nowak, and Cezary Zieliński. Wind impact on UAVs: Experimental study using a multi-fan low-speed wind system. arXiv preprint arXiv:2207.12345, 2022. URL: https://arxiv.org/abs/2207.12345.
  22. Jane Pauline Ramirez and Salua Hamaza. Multimodal locomotion: Next generation aerial–terrestrial mobile robotics. Advanced Intelligent Systems, 5(1):2300327, 2023. Google Scholar
  23. Thomas Stastny and Roland Siegwart. Nonlinear guidance for fixed-wing UAVs in strong wind conditions. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4380-4387. IEEE, 2019. URL: https://doi.org/10.1109/IROS40897.2019.8967814.
  24. Paolo Toth and Daniele Vigo. The vehicle routing problem: latest advances and new challenges. Springer, 2007. Google Scholar
  25. Parthasarathy Velusamy, Santhosh Rajendran, Rakesh Kumar Mahendran, Salman Naseer, Muhammad Shafiq, and Jin-Ghoo Choi. Unmanned aerial vehicles (UAV) in precision agriculture: Applications and challenges. Energies, 15(1):217, 2021. Google Scholar
  26. Zhaohui Yang, Cunhua Pan, Kezhi Wang, and Mohammad Shikh-Bahaei. Energy efficient resource allocation in UAV-enabled mobile edge computing networks. In IEEE International Conference on Communications (ICC), 2020. Google Scholar
  27. Zhe Yu, Yanmin Gong, Shimin Gong, and Yuanxiong Guo. Joint task offloading and resource allocation in UAV-enabled mobile edge computing. In IEEE International Conference on Communications (ICC), 2020. Google Scholar
  28. Yong Zeng, Xiaoli Xu, and Rui Zhang. Trajectory design for completion time minimization in uav-enabled multicasting. IEEE Transactions on Wireless Communications, 17(4):2233-2246, 2018. URL: https://doi.org/10.1109/TWC.2018.2790401.
  29. Yong Zeng, Rui Zhang, and Teng Joon Lim. Throughput maximization for UAV-enabled mobile relaying systems. IEEE Transactions on Communications, 64(12):4983-4996, 2016. URL: https://doi.org/10.1109/TCOMM.2016.2611512.
  30. Chunhua Zhang and John M. Kovacs. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6):693-712, 2012. Google Scholar
  31. Xiao Zhang and Lingjie Duan. Fast deployment of UAV networks for optimal wireless coverage. IEEE Transactions on Mobile Computing, 18(3):588-601, 2019. URL: https://doi.org/10.1109/TMC.2018.2840143.
  32. Xiaohui Zhou, Jing Guo, Salman Durrani, and Halim Yanikomeroglu. Uplink coverage performance of an underlay drone cell for temporary events. In 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1-6, 2018. URL: https://doi.org/10.1109/ICCW.2018.8403634.
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact