Publication: EagleEYE: Aerial Edge-enabled Disaster Relief Response System
Loading...
Identifiers
ISBN: 978-1-7281-4355-2
Publication date
2020-09-20
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Impact
Export
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%.
Description
This paper has been presented at 2020 European Conference on Networks and Communications (EuCNC).
Keywords
Low-latency computing, Object detection, Container, Edge computing
Bibliographic citation
Febrian Ardiansyah, M., William, T., Ibrahiem Abdullaziz, O., Wang, L.C., Tien, P.L. y Yuang, M.C. (2020).EagleEYE: Aerial Edge-enabled Disaster Relief Response System. In 2020 European Conference on Networks and Communications (EuCNC), pp. 321-325.