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CBDN: Cloud-Based Drone Navigation for Efficient Battery Charging in Drone Networks
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- Authors
- Jinyong Kim, Seokhwa Kim, Jaehoon Jeong, Hyoungshick Kim, Jung-Soo Park, Taeho Kim
- Issue Date
- 2019-11
- Citation
- IEEE Transactions on Intelligent Transportation Systems, v.20, no.11, pp.4174-4191
- ISSN
- 1524-9050
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- For long-distance flying, drones often need to charge their battery at quick battery-charging machines (QCMs) because of their limited battery capacity. If a drone individually chooses a QCM without any coordination, a drone network may experience QCM congestion when multiple drones select the same QCM. This QCM congestion may lead to an increasing drone traffic delay. In order to solve this problem, we propose a cloud-based drone navigation (CBDN) system for efficient drone battery charging in drone networks. In order to achieve this goal, the CBDN gathers drone traffic information and determines efficient drone routes so as to minimize the overall QCM congestion level for drone battery charging using cloud-based management. Our key idea is to find globally coordinated drone routes so as to minimize the total traffic delay in a drone network by reducing the overall QCM congestion level. In order to demonstrate the effectiveness of the proposed system, we evaluated the performance of CBDN by simulating a drone network under various network conditions. The simulation results show that CBDN is more efficient than the existing shortest-path-based drone route planning algorithms in terms of end-to-end traffic delay and QCM average utilization.
- KSP Keywords
- Cloud-based management, Congestion level, End to End(E2E), Long distance, Planning algorithm, battery capacity, battery charging, path-based, route planning, shortest path, simulation results