Digital transformation and AI @Edge
Cindy Xing

Bio
- Co-chair K8s IOT/Edge workgroup
- Project lead and code maintainer for
CNCF sandbox project KubeEdge
- Member of K8s community
- Principal Software Engineer at
Microsoft working on Azure IOT Edge
- Years of working experiences in
distributed system
- Passionate on enabling customers and
innovations through Edge Computing

Industrial 4.0
- A new era of industrial revolution
- Advanced manufacturing
techniques
- IOT and AI
- Inter-connect everything
- Digital transformation and
automation
- Data everywhere and data is the
King

What does a manufacturer care about
DESIGN
Provide the new generation
of products with improved
processes and design
INBOUND
LOGISTICS
OPERATION
Apply the best practices to
daily operations of producing
goods
SERVICE
Collect feedback and reiterate
on the next-gen product
AKA, Supply Chain. Provide
parts/ingredients needed for
production
OUTBOUND
LOGISTICS
Deliver goods in the most
efficient way to customer

Step by Step to Digital Transformation
“Pay-as-you-go”,
Outcome-based
Products
Products that never break
“New business models”
Services
7
Digital Feedback
Loops,
Method Calls
How can an autonomous response be achieved?
“Self-optimizing”
Adaptability
6
Long-Term
Storage,
AI Models,
Machine
Learning
What will happen?
“Being prepared”
Predictions
5
Time-
Series/Historical
Data,
Hierarchical
Data Modeling
Why is it happening?
“Understanding”
Transparency
4
Telemetry
Dashboards
What is happening?
“Seeing”
Visibility
3
Edge Gateways
How to Connect?
“Plugging in”
Connectivity
2
PLCs/IPCs
What Data?
“Defining Tags”
Computerization
1
Value
Time

Data Powers
Manufacturing Innovation
Introducing 21st century style product analysis from IT products into
manufacturing industry. Create “Digital Twin” of the physical product including
all its features, parameters, interactions, and lifecycle.
Bridging the gap of all manufacturing operations and achieving informed
products and processes requires building stronger collaboration, breaking
silos, and accessing level-appropriate data freely.
Innovation
Redesign processes to move other
primary activities into production
Data provide visibility and insight on all aspects of
products, processes, and people
Data-informed
company: build
a data-informed
culture
Digitize and digitalize OEE (Overall Equipment Effectiveness), which is the
gold standard to understand, measure, and improve Availability,
Performance, and Quality; And also achieve Prescriptive Maintenance etc.

Industrial 4.0 and Edge
Computing
• Data and intelligence close
to customers
• Short network latency &
quick response
• Higher security and
compliance requirement
• Larger scalability and highly
de-centralized
• Heterogeneous and
diversified
hardware/network settings
• Cloud and Edge integration
• New market, technologies
and opportunities
• Solution for OT/IT together

AI @ Edge
• Rich and large amount of raw
data
• Highly responsive and closer
to real-time
• Enhanced security features to
be added
• Highly de-centralized
• Closer integration of Cloud
and Edge (inferenced AI)
• Superior customer experience

New
Technologies
• Application
• Execution format: container
• Execution environment: docker, lxc, containerd,
cri-o, kata, etc.
• Orchestrator: Kubernetes, mesos, cloudfoundry,
openshift, etc.
• Communication Protocols and Standards: OPC UA,
MQTT, AMQP, Modbus
• Ways to collect and process data
• IOT (Sensor, smart devices, etc.)
• Run inferenced AI model @edge
• Edge computing architecture and projects: Edgex,
Akraino, KubeEdge, K3S, etc.

Rich data modeling preserves source context
Vendors can extend the data model of each
product (Companion Specification)
Maps to industrial protocols, e.g. BACNet |
PLCopen | MTConnect | …
Vendor, Platform and OS Independent
Open Source on GitHub
Discoverable Services Oriented Architecture
(SOA) independent of the transport method
Owned by a Non-Profit (OPC Foundation)
50M installed base and exponential growth
Secure Design from group-up
Based on open security standards
Authentication | Encryption
Evolves as security technologies evolve
Vendors/Users can choose level of security
Easily acceptable by IT departments
Data ModellingInteroperability Security
The Industrial Interoperability Standard

Opensource Edge Projects

Challenges
• Huge amount of data highly
geographically distributed
• Diversified network and
communication protocols
• Real time data processing and data
analytics
• Higher security and compliance risks
• Resource constraint and network
connection bandwidth/reliability
• Infrastructure for OT and IT
• Tooling for application developers and
data scientists

Hackers breach IoT devices to launch an
attack that takes down the internet for a day
IoT attacks on the rise
Hackers infiltrate critical safety systems in
nuclear, oil and gas plants, halting operations
Attackers gain access to casino database
through fish tank’s connected filter device
Mirai Data Breach Triton

SOURCE: "IDC Marketscape: Worldwide Industrial IoT Platforms in Manufacturing 2019 Vendor Assessment" by Stacy Crook and Reid Paquin, June 2019, IDC #US45116819 and IDC #US45116919
IDC Marketscape on Manufacturing and Energy