Modeling and Performance Analysis of Flying Mesh Network
Maintaining good connectivity is a major concern when constructing a robust flying mesh network, known as FlyMesh. In a FlyMesh, multiple unmanned aerial vehicles (UAVs) collaborate to provide continuous network service for mobile devices on the ground. To determine the connectivity probability of t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | http://dx.doi.org/10.1155/2023/8815835 |
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| _version_ | 1849406039285301248 |
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| author | Qin Shenghong Xu Renhui Peng Laixian Wei Xingchen Wu Xiaohui |
| author_facet | Qin Shenghong Xu Renhui Peng Laixian Wei Xingchen Wu Xiaohui |
| author_sort | Qin Shenghong |
| collection | DOAJ |
| description | Maintaining good connectivity is a major concern when constructing a robust flying mesh network, known as FlyMesh. In a FlyMesh, multiple unmanned aerial vehicles (UAVs) collaborate to provide continuous network service for mobile devices on the ground. To determine the connectivity probability of the aerial link between two UAVs, the Poisson point process (PPP) is used to describe the spatial distribution of UAVs equipped with omnidirectional antennas. However, the PPP fails to reflect the fact that there is a minimum distance restriction between two neighboring UAVs. In this paper, the β-Ginibre point process (β-GPP) is adopted to model the spatial distribution of UAVs, with β representing the repulsion between nearby UAVs. Additionally, a large-scale fading method is used to model the route channel between UAVs equipped with directional antennas, allowing the monitoring of the impact of signal interference on network connectivity. Based on the β-GPP model, an analytical expression for the connectivity probability is derived. Numerical tests are conducted to demonstrate the effects of repulsion factor β, UAV intensity ρ, and beamwidth θ on network connectivity. The results indicate that an increase in UAV intensity decreases network connectivity when the repulsion factor β remains constant. These findings provide valuable insights for enhancing the service quality of the FlyMesh. |
| format | Article |
| id | doaj-art-4ee10f37d96946a0a717e625f8b80d45 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-4ee10f37d96946a0a717e625f8b80d452025-08-20T03:36:31ZengWileyInternational Journal of Distributed Sensor Networks1550-14772023-01-01202310.1155/2023/8815835Modeling and Performance Analysis of Flying Mesh NetworkQin Shenghong0Xu Renhui1Peng Laixian2Wei Xingchen3Wu Xiaohui4College of Communication EngineeringCollege of Communication EngineeringCollege of Communication EngineeringCollege of Communication EngineeringCollege of Communication EngineeringMaintaining good connectivity is a major concern when constructing a robust flying mesh network, known as FlyMesh. In a FlyMesh, multiple unmanned aerial vehicles (UAVs) collaborate to provide continuous network service for mobile devices on the ground. To determine the connectivity probability of the aerial link between two UAVs, the Poisson point process (PPP) is used to describe the spatial distribution of UAVs equipped with omnidirectional antennas. However, the PPP fails to reflect the fact that there is a minimum distance restriction between two neighboring UAVs. In this paper, the β-Ginibre point process (β-GPP) is adopted to model the spatial distribution of UAVs, with β representing the repulsion between nearby UAVs. Additionally, a large-scale fading method is used to model the route channel between UAVs equipped with directional antennas, allowing the monitoring of the impact of signal interference on network connectivity. Based on the β-GPP model, an analytical expression for the connectivity probability is derived. Numerical tests are conducted to demonstrate the effects of repulsion factor β, UAV intensity ρ, and beamwidth θ on network connectivity. The results indicate that an increase in UAV intensity decreases network connectivity when the repulsion factor β remains constant. These findings provide valuable insights for enhancing the service quality of the FlyMesh.http://dx.doi.org/10.1155/2023/8815835 |
| spellingShingle | Qin Shenghong Xu Renhui Peng Laixian Wei Xingchen Wu Xiaohui Modeling and Performance Analysis of Flying Mesh Network International Journal of Distributed Sensor Networks |
| title | Modeling and Performance Analysis of Flying Mesh Network |
| title_full | Modeling and Performance Analysis of Flying Mesh Network |
| title_fullStr | Modeling and Performance Analysis of Flying Mesh Network |
| title_full_unstemmed | Modeling and Performance Analysis of Flying Mesh Network |
| title_short | Modeling and Performance Analysis of Flying Mesh Network |
| title_sort | modeling and performance analysis of flying mesh network |
| url | http://dx.doi.org/10.1155/2023/8815835 |
| work_keys_str_mv | AT qinshenghong modelingandperformanceanalysisofflyingmeshnetwork AT xurenhui modelingandperformanceanalysisofflyingmeshnetwork AT penglaixian modelingandperformanceanalysisofflyingmeshnetwork AT weixingchen modelingandperformanceanalysisofflyingmeshnetwork AT wuxiaohui modelingandperformanceanalysisofflyingmeshnetwork |