Optimizing Collaborative UAV Networks for Data Efficiency in IoT Ecosystems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910909121691648 |
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| author | Sharma, Priyavrat Dev Sorkhoh, Ibrahim Maheswaran, Muthucumaru |
| author_facet | Sharma, Priyavrat Dev Sorkhoh, Ibrahim Maheswaran, Muthucumaru |
| contents | Advances in the Internet of Things are revolutionizing data acquisition, enhancing artificial intelligence and quality of service. Unmanned Aerial Vehicles (UAVs) provide an efficient data-gathering solution across varied environments. This paper addresses challenges in integrating UAVs for large scale data operations, including mobility, multi-hop paths, and optimized multi-source information transfer. We propose a collaborative UAV framework that enables efficient data sharing with minimal communication overhead, featuring adaptive power control and dynamic resource allocation. Formulated as an NP-hard Integer Linear Program, our approach uses heuristic algorithms to optimize routing through UAV hubs. Simulations show promise in terms of computation time (99% speedup) and outcome (down to 14% deviation from the optimal). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Collaborative UAV Networks for Data Efficiency in IoT Ecosystems Sharma, Priyavrat Dev Sorkhoh, Ibrahim Maheswaran, Muthucumaru Networking and Internet Architecture Advances in the Internet of Things are revolutionizing data acquisition, enhancing artificial intelligence and quality of service. Unmanned Aerial Vehicles (UAVs) provide an efficient data-gathering solution across varied environments. This paper addresses challenges in integrating UAVs for large scale data operations, including mobility, multi-hop paths, and optimized multi-source information transfer. We propose a collaborative UAV framework that enables efficient data sharing with minimal communication overhead, featuring adaptive power control and dynamic resource allocation. Formulated as an NP-hard Integer Linear Program, our approach uses heuristic algorithms to optimize routing through UAV hubs. Simulations show promise in terms of computation time (99% speedup) and outcome (down to 14% deviation from the optimal). |
| title | Optimizing Collaborative UAV Networks for Data Efficiency in IoT Ecosystems |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2504.08403 |