A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks.
A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks.
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DOI：
10.3390/s18113751
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发表时间：
2018-11-02
期刊：
影响因子：
--
通讯作者：
Ko YB
中科院分区：
文献类型：
--
作者：
Valentino R;Jung WS;Ko YB
无人机最近变得非常受欢迎，特别是在军事和民用应用中。无人机利用的例子包括侦察、监视和数据包传递。随着时间的推移，无人机的任务变得更大、更复杂。因此，蜂群或集群的无人机是首选的，因为它们提供了更大的覆盖范围、灵活性和可靠性。然而，无人机系统的计算能力和能源资源有限，这意味着有时无人机很难按时完成任务。需要一个解决方案来解决这个问题，这样无人机集群才能更快地完成工作。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式计算卸载系统，该系统允许执行高强度任务的无人机集群机会地从附近的其他无人机集群借用计算资源。我们设计了一个基于人工神经网络的响应时间预测模块，用于判断将任务卸载到其他无人机集群是否更快完成任务。只有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。仿真结果表明，我们提出的方案可以通过机会性卸载过程来缩短无人机集群的响应时间。
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.
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