EagleEYE: Aerial edge-enabled disaster relief response system

Muhammad Febrian Ardiansyah, Timothy William, Osamah Ibrahiem Abdullaziz, Li-Chun Wang, Po-Lung Tien, Maria C. Yuang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The fifth generation (5G) mobile network has paved the way for innovations across vertical industries. The integration of distributed intelligent edge into the 5G orchestrated architecture brings the benefits of low-latency and automation. A successful example of this integration is exhibited by the 5G-DIVE project, which aims at proving the technical merits and business value proposition of vertical industries such as autonomous drone surveillance and navigation. In this paper, and as part of 5G-DIVE, we present an aerial disaster relief system, called EagleEYE, which utilizes edge computing and machine learning to detect emergency situations in real-time. EagleEYE reduces training time by devising an object fusion mechanism which enables reusing existing datasets. Furthermore, EagleEYE parallelizes the detection tasks to enable real-time response. Finally, EagleEYE is evaluated in a real-world testbed and the results show that EagleEYE can reduce the inference latency by 90% with a high detection accuracy of 87%.

Original languageEnglish
Title of host publication2020 European Conference on Networks and Communications, EuCNC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages321-325
Number of pages5
ISBN (Electronic)9781728143552
DOIs
StatePublished - Jun 2020
Event29th European Conference on Networks and Communications, EuCNC 2020 - Virtual, Dubrovnik, Croatia
Duration: 15 Jun 202018 Jun 2020

Publication series

Name2020 European Conference on Networks and Communications, EuCNC 2020

Conference

Conference29th European Conference on Networks and Communications, EuCNC 2020
CountryCroatia
CityVirtual, Dubrovnik
Period15/06/2018/06/20

Keywords

  • Container
  • Edge computing
  • Low-latency computing
  • Object detection

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