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Journal Article A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks
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Authors
Rico Valentino, Jung Woosung, 고영배
Issue Date
201811
Source
Sensors, v.18 no.11, pp.1-14
ISSN
1424-8220
Publisher
MDPI AG
DOI
https://dx.doi.org/10.3390/s18113751
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.
KSP Keywords
Artificial Neural Network, Computation offloading, Computational offloading, Computing power, Computing resources, Computing time, Design and simulation, Drone systems, Learning-based, Offloading system, Packet delivery
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
CC BY