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A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

1
Department of Computer Engineering, Ajou University, Suwon 16499, Korea
2
Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
This paper is an extension version of the conference paper: Valentino, R.; Jung, W.; Ko, Y. Opportunistic Computational Offloading System for Clusters of Drones. In Proceedings of the International Conference on Advanced Communication Technology (ICACT’18), ChunCheon, Korea, 11–14 February 2018.
Sensors 2018, 18(11), 3751; https://doi.org/10.3390/s18113751
Received: 2 October 2018 / Revised: 29 October 2018 / Accepted: 29 October 2018 / Published: 2 November 2018
(This article belongs to the Special Issue Unmanned Aerial Vehicle Networks, Systems and Applications)
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Abstract

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. View Full-Text
Keywords: drone cluster; computation offloading; neural network; wireless communication drone cluster; computation offloading; neural network; wireless communication
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Valentino, R.; Jung, W.-S.; Ko, Y.-B. A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks. Sensors 2018, 18, 3751.

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