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Multi-Hop Task Offloading With On-the-Fly Computation for Multi-UAV Remote Edge Computing
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AI
DOI：10.1109/TCOMM.2021.3129902.png)
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Abstract
En 中文
The dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs) foster the recent surge of interests in incorporating UAVs into edge computing systems to facilitate on-demand deployment and extended coverage. Nonetheless, due to the limited communication capability of the UAVs, single-UAV edge computing systems may still be incompetent when serving remote users. Although the traditional multi-UAV relay network can be a viable solution, it fails to exploit the computing capability of the UAVs. With this consideration, a multi-hop task offloading with on-the-fly computation scheme is proposed in this work to enable a more powerful multi-UAV remote edge computing network. To solve the corresponding joint resource allocation and deployment problem, two efficient algorithms are proposed. One of them can find the global optimal strategy in a special case, while the other can obtain a good local optimal strategy in the general cases. Both algorithms have a complexity only linear in the number of UAVs and admit distributed implementation. In addition to analysis, numerical results are provided to corroborate the effectiveness of the proposed scheme.
Keywords:
Task analysis
Edge computing
Servers
Resource management
Unmanned aerial vehicles
Computational modeling
Wireless communication
Edge computing
UAV
resource allocation
deployment
Journal
IF:
8.3
Papers:
1.2W
Citations:
3.6W