Edge-cloud computing performance benchmarking for IoT based machinery vibration monitoring

Ankur Verma; Ayush Goyal; Soundar Kumara; Thomas Kurfess

doi:10.1016/j.mfglet.2020.12.004

Edge-cloud computing performance benchmarking for IoT based machinery vibration monitoring

Ankur Verma, Ayush Goyal, Soundar Kumara, Thomas Kurfess

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

Abstract

Advances in low cost and reliable sensing, connectivity (Internet of Things), computational power, and advanced analytics, are leading to a new wave of innovation in machinery status sensing and condition monitoring. Significant research efforts are directed towards cloud computing architectures. However, given the latency, bandwidth, cost, security, and privacy concerns, further supported by the ever-increasing capabilities of edge computing devices, there is a need to consider both edge and cloud computing together to make informed decisions based upon context and performance. We present an edge-cloud performance evaluation for IoT based machinery vibration monitoring, to foster deployment for the contexts considered.

Original language	English (US)
Pages (from-to)	39-41
Number of pages	3
Journal	Manufacturing Letters
Volume	27
DOIs	https://doi.org/10.1016/j.mfglet.2020.12.004
State	Published - Jan 2021

All Science Journal Classification (ASJC) codes

Mechanics of Materials
Industrial and Manufacturing Engineering

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10.1016/j.mfglet.2020.12.004

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abstract = "Advances in low cost and reliable sensing, connectivity (Internet of Things), computational power, and advanced analytics, are leading to a new wave of innovation in machinery status sensing and condition monitoring. Significant research efforts are directed towards cloud computing architectures. However, given the latency, bandwidth, cost, security, and privacy concerns, further supported by the ever-increasing capabilities of edge computing devices, there is a need to consider both edge and cloud computing together to make informed decisions based upon context and performance. We present an edge-cloud performance evaluation for IoT based machinery vibration monitoring, to foster deployment for the contexts considered.",

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AB - Advances in low cost and reliable sensing, connectivity (Internet of Things), computational power, and advanced analytics, are leading to a new wave of innovation in machinery status sensing and condition monitoring. Significant research efforts are directed towards cloud computing architectures. However, given the latency, bandwidth, cost, security, and privacy concerns, further supported by the ever-increasing capabilities of edge computing devices, there is a need to consider both edge and cloud computing together to make informed decisions based upon context and performance. We present an edge-cloud performance evaluation for IoT based machinery vibration monitoring, to foster deployment for the contexts considered.

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Edge-cloud computing performance benchmarking for IoT based machinery vibration monitoring

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