Modeling Inter-drone Interference as a Service in Skyway Networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911355551875072 |
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| author | Timothy, Gabriel Rizvi, Syeda Amna Umair, Muhammad Bouguettaya, Athman Alkouz, Balsam |
| author_facet | Timothy, Gabriel Rizvi, Syeda Amna Umair, Muhammad Bouguettaya, Athman Alkouz, Balsam |
| contents | We present a novel investigation into the impact of inter-drone interference on delivery efficiencies within multi-drone skyway networks. We conduct controlled experiments to analyze the behavior of drones in an indoor testbed environment. Our study compares performance between solo flights and concurrent multi-drone operations along predefined routes. This analysis captures interference occurring during both flight and at charging stations, providing a comprehensive evaluation of its effects on overall network performance. We conduct a comprehensive series of experiments across diverse scenarios to systematically understand and model the dynamics of inter-drone interference. Key metrics, such as power consumption and delivery times, are considered. This generates a comprehensive dataset for in-depth analysis of interference at both the node and segment levels. These findings are then formalized into a predictive model. The results validate the effectiveness of the developed model, demonstrating its potential to accurately forecast inter-drone interferences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02270 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modeling Inter-drone Interference as a Service in Skyway Networks Timothy, Gabriel Rizvi, Syeda Amna Umair, Muhammad Bouguettaya, Athman Alkouz, Balsam Emerging Technologies We present a novel investigation into the impact of inter-drone interference on delivery efficiencies within multi-drone skyway networks. We conduct controlled experiments to analyze the behavior of drones in an indoor testbed environment. Our study compares performance between solo flights and concurrent multi-drone operations along predefined routes. This analysis captures interference occurring during both flight and at charging stations, providing a comprehensive evaluation of its effects on overall network performance. We conduct a comprehensive series of experiments across diverse scenarios to systematically understand and model the dynamics of inter-drone interference. Key metrics, such as power consumption and delivery times, are considered. This generates a comprehensive dataset for in-depth analysis of interference at both the node and segment levels. These findings are then formalized into a predictive model. The results validate the effectiveness of the developed model, demonstrating its potential to accurately forecast inter-drone interferences. |
| title | Modeling Inter-drone Interference as a Service in Skyway Networks |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2601.02270 |