University of Oulu

G. Lee, W. Saad and M. Bennis, "Online Optimization for UAV-Assisted Distributed Fog Computing in Smart Factories of Industry 4.0," 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 1-6. doi: 10.1109/GLOCOM.2018.8647441

Online optimization for UAV-assisted distributed fog computing in smart factories of industry 4.0

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Author: Lee, Gilsoo1; Saad, Walid1; Bennis, Mehdi2
Organizations: 1Wireless@VT, Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
2Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202003248976
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2020-03-24
Description:

Abstract

In this paper, the problem of unmanned aerial vehicle (UAV)-assisted fog computing in Industry 4.0 smart factories is studied. In particular, a novel online framework is proposed to enable a source UAV to offload computing tasks from ground sensors within a smart factory and allocate them to neighboring fog UAVs for distributed task computing, before the source UAV arrives at its destination. The online nature of the framework allows the UAVs to optimize their task allocation and decide on which neighbors to use for fog computing, even when the tasks are revealed to the source UAV in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the problem, an online greedy algorithm is designed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio can be analytically derived as a function of the task sizes and the data rates of the UAVs. Simulation results show that the proposed online algorithm can achieve a near- optimal task allocation with an optimality gap that is no higher than 7.5% compared to the offline, optimal solution with complete knowledge of all tasks.

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Series: IEEE Global Communications Conference
ISSN: 2334-0983
ISSN-E: 2576-6813
ISSN-L: 2334-0983
ISBN: 978-1-5386-4727-1
ISBN Print: 978-1-5386-4728-8
Pages: 1 - 6
Article number: 8647441
DOI: 10.1109/GLOCOM.2018.8647441
OADOI: https://oadoi.org/10.1109/GLOCOM.2018.8647441
Host publication: 2018 IEEE Global Communications Conference, GLOBECOM 2018
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This research was supported, in part, by the U.S. Office of Naval Research (ONR) under Grant N00014-15-1-2709, by the National Science Foundation under Grant CNS-1739642, by the Academy of Finland project CARMA, by 6Genesis Flagship (grant no. 318927), by the INFOTECH project NOOR, and by the Kvantum Institute strategic project SAFARI.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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