TY - GEN
T1 - An Edge Intelligence Framework for Resource Constrained Community Area Network
AU - Oderhohwo, Ogheneuriri
AU - Mohammed, Hawzhin
AU - Odetola, Tolulope
AU - Guo, Terry N.
AU - Hasan, Syed
AU - Dogbe, Felix
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85090559257&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090559257&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS48704.2020.9184597
DO - 10.1109/MWSCAS48704.2020.9184597
M3 - Conference contribution
AN - SCOPUS:85090559257
T3 - Midwest Symposium on Circuits and Systems
SP - 97
EP - 100
BT - 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020
Y2 - 9 August 2020 through 12 August 2020
ER -