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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks
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A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

机译：基于学习的无人飞行器（UAV）聚类网络机会计算分流的设计与仿真

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摘要

Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones’ tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.
机译：无人机最近变得非常流行，特别是在军事和民用领域。无人机使用的示例包括侦察，监视和数据包传递。随着时间的流逝，无人机的任务变得越来越大，越来越复杂。因此，优选无人机群或无人机群，因为它们可提供更大的覆盖范围，灵活性和可靠性。但是，无人机系统的计算能力和能源有限，这意味着有时候无人机很难按计划完成任务。为此需要一种解决方案，以便无人机集群可以更快地完成其工作。一种可能的解决方案是无人机集群之间的卸载方案。在这项研究中，我们提出了一种机会计算卸载系统，该系统允许具有高强度任务的无人机集群从附近的其他无人机集群中借入计算资源。我们设计了一个基于人工神经网络的响应时间预测模块，用于确定将任务卸载到其他无人机群中是否可以更快地完成任务。仅当预测的卸载响应时间小于本地计算时间时，才执行卸载方案。通过仿真结果表明，我们提出的方案可以通过机会性卸载过程减少无人机群的响应时间。

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