An Edge Intelligence Framework for Resource Constrained Community Area Network

Ogheneuriri Oderhohwo, Hawzhin Mohammed, Tolulope Odetola, Terry N. Guo, Syed Hasan, Felix Dogbe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Edge intelligence, Artificial Intelligence (AI) on the edge can have a significant impact on modern Community Area Network (CAN). This paper proposes an edge intelligence method that utilizes deep learning, object detection, and multi-label multi-classification to perform monitoring and actuation tasks without resorting to high-end edge servers. The proposed method contains a resource-constrained node as an edge device. For the edge server, it utilizes a special-purpose ASIC (Intel's Movidius) interfaced with a node-level edge device. To further the idea of limited bandwidth availability in CAN, pseudo D2D communication is employed. SSD-MobileNet and customized multi-label-multi-classification based GoogLeNet models are hosted on the edge server, The proposed methodology can achieve about 5.26 FPS for complete bi-directional communication.

Original languageEnglish (US)
Title of host publication2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-100
Number of pages4
ISBN (Electronic)9781538629161
DOIs
StatePublished - Aug 2020
Event63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Springfield, United States
Duration: Aug 9 2020Aug 12 2020

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2020-August
ISSN (Print)1548-3746

Conference

Conference63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020
Country/TerritoryUnited States
CitySpringfield
Period8/9/208/12/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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