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Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

An Efficient Connected Swarm Deployment via Deep Learning

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DOI: http://dx.doi.org/10.15439/2021F001

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 17 ()

Full text

Abstract. In this paper, an unmanned aerial vehicles (UAVs) deployment framework based on machine learning is studied. It aims to maximize the sum of the weights of the ground users covered by UAVs while UAVs forming a connected communication graph. We focus on the case where the number of UAVs is not necessarily enough to cover all ground users. We develop an UAV Deployment Deep Neural network (mod) as a UAV's deployment deep network method. Simulation results demonstrate that mod can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and outperform the state-of-the-art traditional algorithms.

References

  1. Haibo Dai, Haiyang Zhang, Baoyun Wang, and Luxi Yang. The multi-objective deployment optimization of uav-mounted cache-enabled base stations. Physical Communication, 34:114–120, 2019.
  2. Kiril Danilchenko and Michael Segal. Connected ad-hoc swarm of drones. In Proceedings of the 6th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, DroNet ’20, New York, NY, USA, 2020. Association for Computing Machinery.
  3. Kiril Danilchenko, Michael Segal, and Zeev Nutov. Covering users by a connected swarm efficiently. In International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics, pages 32–44. Springer, 2020.
  4. Mark De Berg, Sergio Cabello, and Sariel Har-Peled. Covering many or few points with unit disks. Theory of Computing Systems, 45(3):446–469, 2009.
  5. Chien-Chung Huang, Mathieu Mari, Claire Mathieu, Joseph SB Mitchell, and Nabil H Mustafa. Maximizing covered area in the euclidean plane with connectivity constraint. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2019.
  6. Kai Jin, Jian Li, Haitao Wang, Bowei Zhang, and Ningye Zhang. Near-linear time approximation schemes for geometric maximum coverage. Theoretical Computer Science, 725:64–78, 2018.
  7. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint https://arxiv.org/abs/1412.6980, 2014.
  8. Paulo V Klaine, João PB Nadas, Richard D Souza, and Muhammad A Imran. Distributed drone base station positioning for emergency cellular networks using reinforcement learning. Cognitive computation, 10(5):790–804, 2018.
  9. T. Kuo, K. C. Lin, and M. Tsai. Maximizing submodular set function with connectivity constraint: Theory and application to networks. IEEE/ACM Transactions on Networking, 23(2):533–546, 2015.
  10. Jian Li, Haitao Wang, Bowei Zhang, and Ningye Zhang. Linear time approximation schemes for geometric maximum coverage. In International Computing and Combinatorics Conference, pages 559–571, 2015.
  11. Chi Harold Liu, Zheyu Chen, Jian Tang, Jie Xu, and Chengzhe Piao. Energy-efficient uav control for effective and fair communication coverage: A deep reinforcement learning approach. IEEE Journal on Selected Areas in Communications, 36(9):2059–2070, 2018.
  12. Jie Liu, Qiang Wang, Xuan Li, and Wenqi Zhang. A fast deployment strategy for uav enabled network based on deep learning. In 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, pages 1–6, 2020.
  13. Jiangbin Lyu, Yong Zeng, Rui Zhang, and Teng Joon Lim. Placement optimization of uav-mounted mobile base stations. IEEE Communications Letters, 21(3):604–607, 2016.
  14. Mohammad Mozaffari, Walid Saad, Mehdi Bennis, and Mérouane Debbah. Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage. IEEE Comm. Lett., 20(8):1647–1650, 2016.
  15. Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Icml, 2010.
  16. Yu Min Park, Minkyung Lee, and Choong Seon Hong. Multi-uavs collaboration system based on machine learning for throughput maximization. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), pages 1–4. IEEE, 2019.
  17. Luigi Di Puglia Pugliese, Francesca Guerriero, Dimitrios Zorbas, and Tahiry Razafindralambo. Modelling the mobile target covering problem using flying drones. Optimization Letters, 10(5):1021–1052, 2016.
  18. Jin Qiu, Jiangbin Lyu, and Liqun Fu. Placement optimization of aerial base stations with deep reinforcement learning. In ICC 2020-2020 IEEE International Conference on Communications (ICC), pages 1–6. IEEE, 2020.
  19. Sanaz Soltani, Mohammadreza Razzazi, and Hossein Ghasemalizadeh. The most points connected-covering problem with two disks. Theory of Computing Systems, 62(8):2035–2047, 2018.
  20. Anand Srinivas, Gil Zussman, and Eytan Modiano. Construction and maintenance of wireless mobile backbone networks. IEEE/ACM Transactions on Networking, 17(1):239–252, 2009.
  21. Haoran Sun, Xiangyi Chen, Qingjiang Shi, Mingyi Hong, Xiao Fu, and Nicholas D Sidiropoulos. Learning to optimize: Training deep neural networks for interference management. IEEE Transactions on Signal Processing, 66(20):5438–5453, 2018.
  22. Haijun Wang, Haitao Zhao, Weiyu Wu, Jun Xiong, Dongtang Ma, and Jibo Wei. Deployment algorithms of flying base stations: 5g and beyond with uavs. IEEE Internet of Things Journal, 6(6):10009–10027, 2019.
  23. Xiao Zhang and Lingjie Duan. Fast deployment of uav networks for optimal wireless coverage. IEEE Transactions on Mobile Computing, 18(3):588–601, 2018.