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Learning and Batch-Processing Based Coded Computation With Mobility Awareness for Networked Airborne Computing
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AI
DOI：10.1109/TVT.2022.3231179.png)
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Abstract
En 中文
The implementation of many Unmanned Aerial Vehicle (UAV) applications (e.g., fire detection, surveillance, and package delivery) requires extensive computing resources to achieve reliable performance. Existing solutions that offload computation tasks to the ground may suffer from long communication delays. To address this issue, the Networked Airborne Computing (NAC) is a promising technique, which offers advanced onboard airborne computing capabilities by sharing resources among the UAVs via direct flight-to-flight links. However, NAC does not exist yet and enabling it requires overcoming many technical challenges, such as the high UAV mobility, and the uncertain, heterogeneous, and dynamic airspace. This paper addresses these challenges by 1) developing a Dynamic Batch-Processing based Coded Computation (D-BPCC) framework for achieving robust and adaptable cooperative airborne computing, and 2) designing deep reinforcement learning (DRL) based load allocation and UAV mobility control strategies for optimizing the system performance. As the first study to systematically investigate NAC, to the best of our knowledge, we evaluate the proposed methods through designing a NAC simulator and conducting comparative studies with four state-of-the-art distributed computing schemes. The results demonstrate the promising performance of the proposed methods.
Keywords:
Task analysis
Autonomous aerial vehicles
Distributed computing
Vehicle dynamics
Trajectory
Servers
Computational modeling
Networked airborne computing
unmanned aerial vehicle
coded distributed computing
reinforcement learning
load allocation
mobility control
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W
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