Fairness aware multiple drone base station deployment

Fairness aware multiple drone base station deployment

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The recent advances in drone technology significantly improved the effectiveness of applications such as border surveillance, disaster management, seismic surveying, and precision agriculture. The use of drones as base stations to improve communication in the next generation wireless networks is another attractive application. However, the deployment of drone base stations (DBSs) is not an easy task and requires a carefully designed strategy. Fairness is one of the most important metrics of tactical communications or a disaster-affected network and must be considered for the efficient deployment of DBSs. In this study, a fairness-aware multiple DBS deployment algorithm is proposed. As the proposed algorithm uses particle swarm optimisation (PSO) that requires significant processing power, simpler algorithms with faster execution times are also proposed and the results are compared. The simulations are performed to evaluate the performance of the algorithms in two different network scenarios. The simulation results show that the proposed PSO-based method finds the three-dimensional locations of DBSs, achieving the best fairness performance with a minimum number of DBSs for deployment. However, it is shown that the proposed suboptimal algorithm performs very close to the PSO-based solution and requires significantly less processing time.


    1. 1)
      • 1. Sun, Z., Wang, P., Al-Rodhaan, M.A., et al: ‘BorderSense: border patrol through advanced wireless sensor networks’, Ad. Hoc. Netw., 2011, 9, (3), pp. 468477.
    2. 2)
      • 2. Maza, I., Caballero, F., Dios, J.M., et al: ‘Experimental results in multi-UAV coordination for disaster management and civil security applications’, J. Intell. Robot. Syst., 2011, 61, (1–4), pp. 563585.
    3. 3)
      • 3. Sudarshan, K.V.S., Montano, V., Nguyen, A., et al: ‘A heterogeneous robotics team for large-scale seismic sensing’, IEEE Robot. Autom. Lett., 2017, 2, (3), pp. 23773766.
    4. 4)
      • 4. Pederi, Y.A., Cheporniuk, H.S.: ‘Unmanned Aerial Vehicles and new technological methods of monitoring and crop protection in precision agriculture’. IEEE Int. Conf. on Actual Problems of Unmanned Aerial Vehicles Developments, Kiev, Ukraine, December 2015, pp. 298301.
    5. 5)
      • 5. ‘Amazon PrimeAir’,, accessed 10 August 2017..
    6. 6)
      • 6. Al-Hourani, A., Kandeepan, S., Jamalipour, A.: ‘Modeling air-to-ground path loss for low altitude platforms in urban environments’. Proc. 2014 IEEE Global Communications Conf., Austin, TX, USA, December 2014, pp. 28982904.
    7. 7)
      • 7. Al-Hourani, A., Kandeepan, S., Lardner, S.: ‘Optimal LAP altitude for maximum coverage’, IEEE Wirel. Commun. Lett., 2014, 3, (6), pp. 569572.
    8. 8)
      • 8. Mozaffari, M., Saad, W., Bennis, M., et al: ‘Drone small cells in the clouds: design, deployment and performance analysis’. IEEE Global Communications Conf., San Diego, CA, December 2015, pp. 16.
    9. 9)
      • 9. Bor-Yaliniz, R.I., El-Keyi, A., Yanikomeroglu, H.: ‘Efficient 3-D placement of an aerial base station in next generation cellular networks’. IEEE Int. Conf. on Communications, Kuala Lumpur, Malaysia, May 2016, pp. 15.
    10. 10)
      • 10. Kalantari, E., Shakir, M.Z., Yongacoglu, A.: ‘Backhaul-aware robust 3D drone placement in 5G+ wireless networks’. IEEE Int. Conf. on Communications Workshops, Paris, France, May 2017, pp. 109114.
    11. 11)
      • 11. Alzenad, M., El-keyi, A., Lagum, F., et al: ‘3D Placement of an unmanned aerial vehicle base station (UAV-BS) for energy-efficient maximal coverage’, IEEE Wirel. Commun. Lett., 2017, PP, (99), pp. 11.
    12. 12)
      • 12. Mozaffari, M., Saad, W., Bennis, M., et al: ‘Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage’, IEEE Commun. Lett., 2016, 20, (8), pp. 16471650.
    13. 13)
      • 13. Lyu, J., Zeng, Y., Zhang, R., et al: ‘Placement optimization of UAV-mounted mobile base stations’, IEEE Commun. Lett., 2017, 21, (3), pp. 604607.
    14. 14)
      • 14. Kalantari, E., Yanikomeroglu, H., Yongacoglu, A.: ‘On the number and 3D placement of drone base stations in wireless cellular networks’. Proc. IEEE 63rd Vehicular Technology Conf., Montreal, Canada, September 2016, pp. 16.
    15. 15)
      • 15. Cirik, A.C.: ‘Fairness considerations for full duplex multi-user MIMO systems’, IEEE Wirel. Commun. Lett., 2015, 4, (4), pp. 361364.
    16. 16)
      • 16. Calabuig, D., Monserrat, J.F., Cardona, N.: ‘Fairness-driven fast resource allocation for interference-free heterogeneous networks’, IEEE Commun. Lett., 2012, 16, (7), pp. 10921095.
    17. 17)
      • 17. Eberhart, R., Kennedy, J.: ‘A new optimizer using particle swarm theory’. Proc. 6th Int. Symp. on Micro Machine and Human Science, Nagoya, Japan, October 1995, pp. 3943.
    18. 18)
      • 18. Kanungo, T., Mount, D.M., Netanyahu, N.S., et al: ‘An efficient k-means clustering algorithm: analysis and implementation’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 881892.
    19. 19)
      • 19. Lloyd, S.P.: ‘Least squares quantization in PCM’, IEEE Trans. Inf. Theory, 1982, 28, (2), pp. 129137.
    20. 20)
      • 20. Pelleg, D., Moore, A.W.: ‘X-means: extending K-means with efficient estimation of the number of clusters’. Proc. 7th Int. Conf. on Machine Learning, San Francisco, CA, USA, June 2000, pp. 727734.
    21. 21)
      • 21. Shi, H., Prasad, R.V., Onur, E., et al: ‘Fairness in wireless networks: issues, measures and challenges’, IEEE Commun. Surv. Tut., 2014, 16, (1), pp. 524.
    22. 22)
      • 22. Jain, R., Chiu, D., Hawe, W.: ‘A quantitative measure of fairness and discrimination for resource allocation in shared systems’. Tech. Rep. DEC-TR-301, September 1984.

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