edge patterns in the iiot

EDGE PATTERNS IN THE IIOTBRAD NICHOLASCHICAGO IOT MEETUPMARCH 2017

2Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx

AGENDA

01 3 minutes about Uptake02 Some key considerations03 The 3 patterns04 Manufacturing discussion / Q&A


Uptake at a glance

AEROSPACE AGRICULTURE CONSTRUCTION ENERGY

104Mpredictions generated to date

2014founded 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 PredictionAnomaly Detection

RecommendationsEvent / Alert Filtering

Data Operations CenterNormalization & Cleansing

End to end visibilityEncryption in transit and at rest

API PortalDeveloper 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/uptakeCome see me if you’re interested in IoT, device management, embedded programming, crypto

Key Considerations02


Digitization is lagging in many industry sectors that need IIoT

MGI Industry Digitization Indexhttp://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 approachSensing vs ActuatingDifferent time horizonsOpen 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 essentialKey to managing complexity, achieving maintainability and resiliencehttps://effectivesoftwaredesign.com/2012/02/05/separation-of-concerns/

IP protection is crucialData rights management for both original and derived data, at rest and in flight, all nodes, including authorized usehttps://motherboard.vice.com/en_us/article/why-american-farmers-are-hacking-their-tractors-with-ukrainian-firmware

Heterogeneity is unavoidableComputing environmentsNode stateMobility vs fixed locationNetworking options and node availabilityDomain responsibility

IT/OT barrier is literally a real thingOperational control comes firstSkills/expertise is very differentMost capital equipment is decades old and relies on physical securityhttp://blog.iiconsortium.org/2016/08/it-vs-ot-for-the-industrial-internet-two-sides-of-the-same-coin.html

The 3 patterns03


3 patterns seem to address most IIoT deployment scenarios

Physical EdgeOn-device IoT node

Platform & Applications

Cloud Edgereverse 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 streamsInteracts with Edge Gateways and higher end Physical Edge nodesServes 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 processProtects machine / process operation at all costs, even if data extraction compromisedRuns on-machine / on-process analytics functionsProtects 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 operatorsProvides much richer data access capabilities


The Edge Gateway

Resides in proximity to physical web nodes and handles connectivity gapsManages “inter physical web” IoT interactions that aren’t needed to control things

Primary function is to monitor physical web machines / processesEliminates the need for physical web devices to interact with the Cloud Edge directlyQueues on premise when backhaul connectivity is unavailable, restricted due to cost or otherwise unusableSpeaks 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 EffectivenessTotal Productive MaintenanceSeiichi Nakajima 1982-1984

www.AMTonline.orghttp://capstonemetrics.com/files/whitepaper-oeeoverview.pdf

OEE = Availability x Performance x QualityTEEP = 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?


DiscussionSome potential improvements - beyond normal operations

Physical Web node functionsVibration analytics for rotational and reciprocating machineryAdditional process quality instrumentationDetailed / granular OEE data collection via SCADA and machine control integrations

Physical Web and Cloud Edge node functionsEvent 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 managementCompute capacitySystem operations

Additional material:McKinsey Analytics Reporthttp://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 Computinghttp://a16z.com/2016/12/16/the-end-of-cloud-computing/

Frank Chen – Deep Learning and Machine Learning Primerhttp://a16z.com/2016/06/10/ai-deep-learning-machines/

Thank You

edge patterns in the iiot

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