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The coverage method of unmanned aerial vehicle mounted base station sensor network based on relative distance

Author

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  • Taifei Zhao
  • Hua Wang
  • Qianwen Ma

Abstract

The unmanned aerial vehicle features with high flexibility and easy deployment. It could be used as an air base station and provide fast communication services for the ground users. It plays an important role in some special occasions such as natural disasters, emergency communications and temporary large-scale activities. A single unmanned aerial vehicle equipped with base station has limited range of services, but a multiple unmanned aerial vehicle equipped with base station network can serve a wider range of users. The research goal of unmanned aerial vehicle equipped with base station network coverage control is to maximize the network coverage under the condition of maintaining the service quality. In view of the low dynamic coverage ratio of unmanned aerial vehicle equipped with base station network, this article proposes a relative distance–based unmanned aerial vehicle equipped with base station deployment method. The unmanned aerial vehicle realizes on-demand coverage and maintains a stable network topology under the influence of three relative distances by sensing the uncovered area of the ground, the neighbouring unmanned aerial vehicles and the location of the coverage boundary or obstacles. In addition, the algorithm is also adapted to a variety of scenarios. The simulation results show that the coverage of the proposed algorithm is 22.4% higher than that of random deployment, and it is 9.9%, 4.7% and 2.1% higher than similar virtual force-oriented node, circular binary segmentation and hybrid local virtual force algorithms.

Suggested Citation

  • Taifei Zhao & Hua Wang & Qianwen Ma, 2020. "The coverage method of unmanned aerial vehicle mounted base station sensor network based on relative distance," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
  • Handle: RePEc:sae:intdis:v:16:y:2020:i:5:p:1550147720920220
    DOI: 10.1177/1550147720920220
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    Cited by:

    1. Tamer Ahmed Farrag & M. A. Farag & Rizk M. Rizk-Allah & Aboul Ella Hassanien & Mostafa A. Elhosseini, 2023. "An improved parallel processing-based strawberry optimization algorithm for drone placement," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(2), pages 245-275, February.

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