TY - GEN
T1 - Big Data Dimensionality Reduction at IoT Edge through Optical Graph Signal Processing
AU - Khodaei, Alireza
AU - Deogun, Jitender
AU - Alexander, Dennis
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/11
Y1 - 2020/12/11
N2 - We introduce an innovative edge computing paradigm for big data dimensionality reduction in IoT networks through employing an underlying Graph Signal Processing (GSP) model-alternatively to von Neumann architecture for conventional computing. We realize a new breed of graph filters by instituting photonic wave interference in standard WDM optical communication systems, and thereby, we embody GSP in an optical computation context. Our proposed filter instantaneously takes the average of large-scale multidimensional data while is in transit from IoT edge to its core. Through co-using the communication resources for computation, our work motivates a modern IoT edge computing paradigm based on using a collective pool of computation and communication resources. Our solution saves a significant amount of processing delay, investment in cloud structure, and carbon footprint. It is, therefore, instrumental for breaking'the curse of dimensionality' in complex IoT applications such as self-driving vehicles that involve deploying lots of edge nodes and handling large volumes of high dimensional data with very low latency.
AB - We introduce an innovative edge computing paradigm for big data dimensionality reduction in IoT networks through employing an underlying Graph Signal Processing (GSP) model-alternatively to von Neumann architecture for conventional computing. We realize a new breed of graph filters by instituting photonic wave interference in standard WDM optical communication systems, and thereby, we embody GSP in an optical computation context. Our proposed filter instantaneously takes the average of large-scale multidimensional data while is in transit from IoT edge to its core. Through co-using the communication resources for computation, our work motivates a modern IoT edge computing paradigm based on using a collective pool of computation and communication resources. Our solution saves a significant amount of processing delay, investment in cloud structure, and carbon footprint. It is, therefore, instrumental for breaking'the curse of dimensionality' in complex IoT applications such as self-driving vehicles that involve deploying lots of edge nodes and handling large volumes of high dimensional data with very low latency.
KW - Graph Signal Processing
KW - IoT
KW - big data
KW - edge computing
KW - optical signal processing
KW - ultrashort laser
UR - http://www.scopus.com/inward/record.url?scp=85101723103&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101723103&partnerID=8YFLogxK
U2 - 10.1109/ICCC51575.2020.9345213
DO - 10.1109/ICCC51575.2020.9345213
M3 - Conference contribution
AN - SCOPUS:85101723103
T3 - 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020
SP - 2491
EP - 2495
BT - 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Computer and Communications, ICCC 2020
Y2 - 11 December 2020 through 14 December 2020
ER -