Next Article in Journal
Amphiphilic Fluorine-Containing Block Copolymers as Carriers for Hydrophobic PtTFPP for Dissolved Oxygen Sensing, Cell Respiration Monitoring and In Vivo Hypoxia Imaging with High Quantum Efficiency and Long Lifetime
Next Article in Special Issue
A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption
Previous Article in Journal
Joint Sparsity Constraint Interferometric ISAR Imaging for 3-D Geometry of Near-Field Targets with Sub-Apertures
Previous Article in Special Issue
An Augmented Reality Geo-Registration Method for Ground Target Localization from a Low-Cost UAV Platform
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle

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

Department of Computer Engineering, Ajou University, Suwon 16499, Korea
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;
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)
PDF [1957 KB, uploaded 2 November 2018]


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top