Edge patterns in the IIoT

EDGE PATTERNS IN THE IIOT
BRAD NICHOLAS
CHICAGO IOT MEETUP
MARCH 2017

2Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
AGENDA
01 3 minutes about Uptake
02 Some key considerations
03 The 3 patterns
04 Manufacturing discussion / Q&A

Uptake at a glance
AEROSPACE AGRICULTURE CONSTRUCTION ENERGY
104M
predictions
generated to date
2014
founded in Chicago
82%
across Data Science
& Engineering
700 Employees
Uptake has developed partnerships in:
HEALTHCARE MINING RAIL RETAIL
Uptake selected as the hottest
startup of 2015 – beating out
Uber and Slack. – Dec 2015
Uptake’s Industry Thought Leaders featured in:

Our platform is purpose-built to deliver actionable insights and
recommendations into workflow, empowering people to create value
Raw Data Data Ingestion Platform Apps
Data Science Engines
Data Integrity
Software Development Kit
Failure Prediction
Anomaly Detection
Recommendations
Event / Alert Filtering
Data Operations Center
Normalization &
Cleansing
End to end visibility
Encryption in transit
and at rest
API Portal
Developer
Content
Mgmt.
App Store
Tools
Assets
Customers
ERP
Contextual
• Weather
• Social Media
• 3rd party
Sample Apps:
• Condition-
Based
Monitoring
• Supply Chain
Optimization
• Fuel and
Energy
Management
• Performance
Optimization
Workflow Integration
Examples:
• Automated
locomotive
re-routing
• Automated
parts
ordering
• Automated
maintenance
scheduling
END-TO-END CYBER, INFORMATION, AND OPERATIONAL SECURITY

About Me
I run the IoT team at Uptake
bradn
www.linkedin.com/in/bradn
Automotive, Manufacturing,
Consulting, Telecom, Startups
EE MBA
Fun fact: I “OEM+” hack & restore
German cars

We’re hiring.
https://boards.greenhouse.io/uptake
Come see me if you’re interested in IoT, device management, embedded programming, crypto

Digitization is lagging in many industry sectors that need IIoT
MGI Industry Digitization Index
http://www.mckinsey.com/industries/high-tech/our-insights/digital-america-a-tale-of-the-haves-and-have-mores
• Quasi-public and/or highly localized
sectors are lagging in digitization
• Labor-intensive sectors need digital
tools for the workforce
• Knowledge-intensive sectors are
already highly digitized
• Capital-intensive sectors have high
IoT potential
• Service sectors can digitize customer
transactions
• B2B sectors can benefit from
expanded digital engagement
6
5
1
2
3
4

3 essential elements to IIoT value creation
Data Ingestion
“Sense”
Analytics
“Infer”
Workflow
“Act”

IIC’s reference model for industrial analytics covers most of the bases
Multi-tiered approach
Sensing vs Actuating
Different time horizons
Open vs Closed loop
Source: Industrial Internet Consortium IIRA http://www.iiconsortium.org/IIRA.htm

Where you compute affects many things
There is no one architecture that will address everything.
But there are certainly some common questions to answer
Proximity
Response
Time
Node
Computing
Capacity
Bandwidth
Consumed
Focal
Points
Exceptions
Sense Act

How you are able to connect also affects what you can do
Latency, bandwidth, cost and complexity are usually not as optimal as you want them to be
MobileLocal IndividualSite

Other key IIoT needs, beyond strong security & viable economics
Separation of Concerns is essential
Key to managing complexity, achieving
maintainability and resilience
https://effectivesoftwaredesign.com/2012/02/05/separation-of-concerns/
IP protection is crucial
Data rights management for both original
and derived data, at rest and in flight, all
nodes, including authorized use
https://motherboard.vice.com/en_us/article/why-american-farmers-are-
hacking-their-tractors-with-ukrainian-firmware
Heterogeneity is unavoidable
Computing environments
Node state
Mobility vs fixed location
Networking options and node availability
Domain responsibility
IT/OT barrier is literally a real thing
Operational control comes first
Skills/expertise is very different
Most capital equipment is decades old and
relies on physical security
http://blog.iiconsortium.org/2016/08/it-vs-ot-for-the-industrial-internet-two-
sides-of-the-same-coin.html

3 patterns seem to address most IIoT deployment scenarios
Physical Edge
On-device IoT node
Platform &
Applications
Cloud Edge
reverse CDN for
the physical web
Edge Gateway
“On location”
connectivity node

The Cloud Edge is effectively the ‘virtual physical web’
A hybrid node that serves as a “concentrator” or “reverse CDN” for the physical web.
It can isolate IoT traffic and service cloud-based applications with anything they need from the
physical web
Concentrates physical web data streams
Interacts with Edge Gateways and higher end Physical Edge nodes
Serves web APIs to cloud applications
You can train ML using the data on this node.
You could continually train ML given sufficient compute capacity and data.
You can distribute its contents via CDN, subject to data rights management

The Physical Edge interacts directly with IIoT data sources
Protects the OT layer and hosts specialized, “high interaction” IoT processes
Serves as a direct data extraction point for physical web data generated by a machine or process
Protects machine / process operation at all costs, even if data extraction compromised
Runs on-machine / on-process analytics functions
Protects OEM and machine owner IP by enforcing data rights management at the source, under terms
suitable to the IP owners
Must be designed and deployed in collaboration with machine / process OEMs and operators
Provides much richer data access capabilities

The Edge Gateway
Resides in proximity to physical web nodes and handles connectivity gaps
Manages “inter physical web” IoT interactions that aren’t needed to control things
Primary function is to monitor physical web machines / processes
Eliminates the need for physical web devices to interact with the Cloud Edge directly
Queues on premise when backhaul connectivity is unavailable, restricted due to cost or otherwise unusable
Speaks local machine dialects

The 3 edge patterns can be implemented flexibly
Physical Edge nodes deployed on advanced machines with excellent connectivity can connect
directly to Cloud Edge nodes – without an Edge Gateway
Cloud Edge nodes could be deployed anywhere connectivity to other edge nodes and “data
center quality” bandwidth is available
• A very high end physical web machine or process
• At a fixed location like an airport terminal
Edge Gateway nodes could be co-deployed with Physical Web nodes as long as suitable
backhaul connectivity to a Cloud Edge node is available

Discussion
Applying the edge patterns in manufacturing
04

OEE - Overall Equipment Effectiveness
Total Productive Maintenance
Seiichi Nakajima 1982-1984
www.AMTonline.org
http://capstonemetrics.com/files/whitepaper-oeeoverview.pdf
OEE = Availability x Performance x Quality
TEEP = Loading x OEE

OEE & TEEP let you keep score, but that’s about it.
• They’re useful, but reactive, not predictive
• What are the historical causes of poor OEE? Are they clearly
understood? Are they static or do things change over time?
• Are there ways to recognize patterns in historical data that can provide
advanced indication of those causes developing?
• Can you act on those causes?
• How much time would you have to act?
• What would you need to improve your ability to predict issues and act
on those predictions?

Discussion
Some potential improvements - beyond normal operations
Physical Web node functions
Vibration analytics for rotational and reciprocating machinery
Additional process quality instrumentation
Detailed / granular OEE data collection via SCADA and machine control integrations
Physical Web and Cloud Edge node functions
Event correlation analytics

This is the tip of the iceberg
A lot of critical questions have been left unanswered here. Great discussion topics!
Greenfield vs Brownfield (factory fit vs retrofit)
Remote device management
Compute capacity
System operations
Additional material:
McKinsey Analytics Report
http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world?cid=analytics-alt-mgi-mgi-oth-1612
Peter Levine – The End of Cloud Computing
http://a16z.com/2016/12/16/the-end-of-cloud-computing/
Frank Chen – Deep Learning and Machine Learning Primer
http://a16z.com/2016/06/10/ai-deep-learning-machines/

Edge patterns in the IIoT

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