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The Optimal Trajectory Planning for UAV in UAV-aided Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10040))

Abstract

Wireless Sensor Networks (WSNs) have been increasingly deployed in harsh environments for special applications such as ecological monitoring and Volcano monitoring. Harsh environments can easily cause the network to be unconnected, which lead to unable to collect data by multi-hops. The development of the Unmanned Aerial Vehicles (UAVs) makes it possible to collect data from ground sensor nodes by UAVs, and the UAV trajectory planning is necessary for energy conservation and data collection efficiency. The challenge of the trajectory planning is keeping the trajectory as short as possible while ensures the communication constraints to be satisfied. In this paper, we formulate the optimal UAV trajectory planning problem to a mixed integer programming (MIP) problem, and develop a heuristic algorithm to find a feasible solution to this problem. There are four steps of our scheme: initialization, rotation, optimization and smooth. The simulation results show that our trajectory planning scheme can shorten the length of the UAV’s trajectory while satisfy communication constraints for every sensor nodes.

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References

  1. Abdulla, A.E., Fadlullah, Z.M., Nishiyama, H., Kato, N., Ono, F., Miura, R., et al.: An optimal data collection technique for improved utility in uas-aided networks. In: 2014 IEEE Proceedings on INFOCOM, 736–744. IEEE (2014)

    Google Scholar 

  2. Martinez-de, J.R., Dios, J.R., Lferd, K., de San Bernabé, A., Núnez, G., Torres-González, A., Ollero, A.: Cooperation between uas and wireless sensor networks for efficient data collection in large environments. J. Intell. Robot. Syst. 70(1–4), 491–508 (2013)

    Google Scholar 

  3. Ho, D.-T., Grøtli, E.I., Sujit, P., Johansen, T.A., Sousa, J.B.: Optimization of wireless sensor network and uav data acquisition. J. Intell. Robot. Syst. 78(1), 159–179 (2015)

    Article  Google Scholar 

  4. Wichmann, A., Korkmaz, T.: Smooth path construction and adjustment for multiple mobile sinks in wireless sensor networks. Comput. Commun. 72, 93–106 (2015)

    Article  Google Scholar 

  5. Alejo, D., Cobano, J.A., Heredia, G., Martínez-de Dios, J.R., Ollero, A.: Efficient trajectory planning for WSN data collection with multiple UAVs. In: Koubâa, A., Martínez-de Dios, J.R. (eds.) Cooperative Robots and Sensor Networks 2015. SCI, vol. 604, pp. 53–75. Springer, Heidelberg (2015). doi:10.1007/978-3-319-18299-5_3

    Chapter  Google Scholar 

  6. Wang, C., Ma, F., Yan, J., De, D., Das, S.K.: Efficient aerial data collection with uav in large-scale wireless sensor networks. Int. J. Distrib. Sens. Netw. 2015, 19 (2015)

    Google Scholar 

  7. Sujit, P., Lucani, D.E., Sousa, J.B.: Bridging cooperative sensing and route planning of autonomous vehicles. IEEE J. Sel. Areas Commun. 30(5), 912–922 (2012)

    Article  Google Scholar 

  8. Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 1403–1416 (2015)

    Article  MathSciNet  Google Scholar 

  9. Gu, B., Sun, X., Sheng, V.S.: Structural minimax probability machine. IEEE Trans. Neural Netw. Learn. Syst. (2016)

    Google Scholar 

  10. Xie, S., Wang, Y.: Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wireless Pers. Commun. 78(1), 231–246 (2014)

    Article  Google Scholar 

  11. Teh, S.K., Mejias, L., Corke, P., Hu, W.: Experiments in integrating autonomous uninhabited aerial vehicles (uavs) and wireless sensor networks (2008)

    Google Scholar 

  12. Kaur, D., Murugappan, M.: Performance enhancement in solving traveling salesman problem using hybrid genetic algorithm. In: Fuzzy Information Processing Society: NAFIPS 2008. Annual Meeting of the North American, pp. 1–6. IEEE (2008)

    Google Scholar 

  13. Yang, K., Sukkarieh, S.: 3d smooth path planning for a uav in cluttered natural environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 794–800. IEEE (2008)

    Google Scholar 

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Correspondence to Quan Wang .

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Wang, Q., Chang, X. (2016). The Optimal Trajectory Planning for UAV in UAV-aided Networks. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-48674-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48673-4

  • Online ISBN: 978-3-319-48674-1

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