Joint Location Selection and Supply Allocation for UAV Aided Disaster Response System

  • Nanxin WangEmail author
  • Jingheng ZhengEmail author
  • Jihong TongEmail author
  • Kai ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11743)


Unmanned aerial vehicles (UAVs) attract public attention because of its mobility, transportability and agility. UAVs can be assistants in disaster relief operation, and such a transportable disaster response UAV system is supposed to take responsibility for both reconnaissance and delivery of supplies. There are many researches about UAV system used when disasters occur. Nevertheless, how to identify the deployment locations where UAVs take off is seldom considered. Furthermore, the space for deployment is limited because of the adverse condition of the ground, but there will be a large requirement of disaster relief supplies. There is an urgent need for utilizing limited space to store as many as possible required supplies delivered to destinations. Aiming to solve the first problem, we identify the best locations for deployment, which makes the disaster response system reconnoiter as many as possible main roads when promising to delivery supplies to as many as possible destinations in some areas. For the second one, we propose an algorithm to maximize the space utilization, which allows the system to store more supplies in a given space.


Location selection Supply allocation UAV Disaster response system 


  1. 1.
    Wang, J., Jiang, C., Han, Z., Ren, Y., Maunder, R.G., Hanzo, L.: Taking drones to the next level: cooperative distributed unmanned-aerial-vehicular networks for small and mini drones. IEEE Veh. Technol. Mag. 12(3), 73–82 (2017)CrossRefGoogle Scholar
  2. 2.
    Wang, J., Jiang, C., Wei, Z., Bai, T., Zhang, H., Ren, Y.: UAV aided network association in space-air-ground communication networks. In: 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, pp. 1–6 (2018)Google Scholar
  3. 3.
    Lin, Y., Hyyppa, J., Rosnell, T., Jaakkola, A., Honkavaara, E.: Development of a UAV-MMS-collaborative aerial-to-ground remote sensing system – a preparatory field validation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(4), 1893–1898 (2013)CrossRefGoogle Scholar
  4. 4.
    Erdelj, M., Natalizio, E., Chowdhury, K.R., Akyildiz, I.F.: Help from the sky: leveraging UAVs for disaster management. IEEE Pervasive Comput. 16(1), 24–32 (2017)CrossRefGoogle Scholar
  5. 5.
    Wu, Q., Zeng, Y., Zhang, R.: Joint trajectory and communication design for multi-UAV enabled wireless networks. IEEE Trans. Wirel. Commun. 17(3), 2109–2121 (2018)CrossRefGoogle Scholar
  6. 6.
    Wang, J., Jiang, C., Wei, Z., Pan, C., Zhang, H., Ren, Y.: Joint UAV hovering altitude and power control for space-air-ground IoT networks. IEEE Internet Things J. 6(2), 1741–1753 (2019)CrossRefGoogle Scholar
  7. 7.
    Hu, Z., Zheng, Z., Song, L., Wang, T., Li, X.: UAV offloading: spectrum trading contract design for UAV-assisted cellular networks. IEEE Trans. Wirel. Commun. 17(9), 6093–6107 (2018)CrossRefGoogle Scholar
  8. 8.
    Rahnama, E., Asaadi, M., Parto, K.: PRE-flight checks of navigation systems and PAPI lights using a UAV. In: 2018 Integrated Communications, Navigation, Surveillance Conference (ICNS), Herndon, VA, pp. 2B4-1–2B4-7, April 2018Google Scholar
  9. 9.
    Keyworth, S., Wolfe, S.: UAVS for land use applications: UAVs in the civilian airspace institution of engineering and technology. In: IET Seminar on UAVs in the Civilian Airspace, London, pp. 1–13, March 2013Google Scholar
  10. 10.
    Shah, M.Z., Samar, R., Bhatti, A.I.: Guidance of air vehicles: a sliding mode approach. IEEE Trans. Control Syst. Technol. 23(1), 231–244 (2015)CrossRefGoogle Scholar
  11. 11.
    Chazelle, B.: The bottomn-left bin-packing heuristic: an efficient implementation. IEEE Trans. Comput. C–32(8), 697–707 (1983)CrossRefGoogle Scholar
  12. 12.
    Liu, D., Teng, H.: An improved BL-algorithm for genetic algorithm of the orthogonal packing of rectangles. Eur. J. Oper. Res. 112(2), 413–420 (2007)CrossRefGoogle Scholar
  13. 13.
    Wang, J., Jiang, C., Ni, Z., Guan, S., Yu, S., Ren, Y.: Reliability of cloud controlled multi-UAV systems for on-demand services. In: IEEE Global Communications Conference (GLOBECOM), Singapore, pp. 1–6, December 2017Google Scholar
  14. 14.
    Jiang, C., Chen, Y., Gao, Y., Liu, K.J.R.: Joint spectrum sensing and access evolutionary game in cognitive radio networks. IEEE Trans. Wirel. Commun. 12(5), 2470–2483 (2013)CrossRefGoogle Scholar
  15. 15.
    Jiang, C., Chen, Y., Liu, K.J.R., Ren, Y.: Renewal-theoretical dynamic spectrum access in cognitive radio network with unknown primary behavior. IEEE J. Sel. Areas Commun. 31(3), 406–416 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.International SchoolBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.School of Information EngineeringEastern Liaoning UniversityDandongChina
  4. 4.Department of Electronic EngineeringTsinghua UniversityBeijingChina

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