The Internet of Things (IoT) keeps evolving, and there’s a critical need for high-speed data processing, analytics, and reduced latency at the edge. Meeting the needs of these systems that leverage a distributed architecture to bring compute resources to the edge and the cloud is essential. A cloud-only model might not be applicable for time-sensitive operations or where network connectivity is poor. Also, connecting every device to the cloud and sending raw data over the internet can have privacy, security, and legal implications, especially for sensitive data. Learn how AWS extends AWS Greengrass to devices, so they can act locally on data and use the cloud for management, analytics, and durable storage.

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AWS re:INVENT
Edge Intelligence for IoT Applications
D r . J o s e f W a l t l – G l o b a l S e g m e n t L e a d e r – I n d u s t r i a l S o f t w a r e
J o s e p h Z a l o k e r – N o r t h A m e r i c a I o T S e g m e n t L e a d e r

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Introduction to Edge Computing

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Why Edge Compute

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Edge Fog and Cloud Computing
Low Latency
Low Volume
High Latency
High Volume
Technique Need Response Applications
Cloud
Massive datasets for long
term analysis
Days to Minutes • Deep Learning
Fog
M2M communication and
analysis of batch data from
multiple devices
Minutes to Milliseconds • Industrial
• Wearables – Data
Tracking
Edge
Real-time decision making
off of one devices data
Milliseconds to
Nanoseconds
• Real-time traffic
Monitoring
• Robotics
• Streaming Video
• VR/AR

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Business Drivers
Bandwidth constrained network
Lack of connectivity
Real-time decisions
Security & Privacy

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Benefits of Edge Computing
Respond quickly
to local events
Simplified device
programming
Reduce the cost of
Ownership
Security and
Privacy
Operate
offline

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The Opportunity

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Market Opportunity
Source McKinsey

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Market Opportunity
Source McKinsey

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Ke y ap p lications V ision, R ob otics & Patte rn R e cog nition
Source McKinsey

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Edge Compute Architecture

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Current Compute Model
Data Storage
Data Processing
ML/Analytics
Data Capture
Data Driven
Action
Data Aggregation
Data Transport
Cloud/Data Center

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Edge Compute Model
Data Capture
Data Driven
Action
Storage
Real-Time
Processing
Data Aggregation
Data Transport
Data Storage
Data Analytics
Cloud/Data Center
Data Storage
Data Processing
Deep Learning
Device to Device Communications

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Edge Devices
• Run compute workloads, like Lambda functions, directly on device.
• Process data/run functions without permanent connection to the
cloud.
• Only subset(s) of data travels to the cloud for further processing.
• Finite capacity – share workloads with cloud when necessary.

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Regional and Metro “Edge” Data Centers
• Regional and metro data centers/interconnect label themselves
“edge.”
• Maybe located closer to end users/devices, but not truly “edge”.
• Wireless network access via multiple hops, central offices and data
lakes

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Mobile Edge Computing or Multi-Access
Edge Computing (MEC):
• ETSI promoting multi-vendor wireless technology called MEC.
• Standard wireless network interface.
• Allows workloads to interface directly with the wireless base
station.

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What does Edge bring to the Industrial Space?

18. Industrial Software Needs Integration with the Field
Level to Maximize Value
Digital twin with Bill of Material / Bill of Processes
Digital twin of status of assets – Manufacturing Optimization Management, Industrial IoT

19. Digital twin of status of assets – Manufacturing Optimization Management, Industrial IoT
Operation
Technology
• High lead time to value  digital twin for production code
• Time critical processes  real time control
• Mass data, low bandwidth  pre processing
• Limited compute on field level  use of unlimited cloud resources
• High cost of automation HW  use of standard, minimal HW
Industrial Software Needs Integration with the Field
Level to Maximize Value

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Pushing the Boundary to Control on the Edge

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Edge Powered Industrial Control

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CUSTOMER SUCCESS STORIES

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Customer Success Story
Rio Tinto has connectivity
challenges at some of the mine
locations where large, expensive
machinery is in play. Rio was
looking for a way to still leverage
the cloud to predict failures and
learn from their environment.
Problem Solution
Rio is using edge computing to
calculate road roughness from
sensors on haul trucks and create
an online heat map of the rough
roads. Maintenance crews will use
this information to effect road
repairs and reduce premature
damage of their machinery.
Unlike current on premise programs for monitoring the machinery, GG allows
for real-time alerts and machine-to-machine communication while leveraging
Machine Learning models in the cloud when connectivity is available.
Impact

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Customer Success Story
Problem Solution
Impact
Pentair provides beer and water
filtration systems to large industrial
brewing customers like Heineken.
Most of their industrial customers are
located in remote geographies with
unreliable internet connections. They
also have customers who do not want
to open up firewalls port for internet
connectivity.
They want to phase out or integrate their
current PLCs with GG clusters to make real-
time decisions on premise and eventually
streaming to the cloud for further analysis.
Pentair can take this use case and replicate it across their various workloads in
commercial, residential and industrial spaces. Taking the cloud models and, when
needed, pushing them into local environments.

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How AWS Helps Partners

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How AWS Helps Partners
• Provide innovative services for Partners to build upon
• Enable Partner-to-Partner opportunities
• Onsite training and support
• Co-fund innovative, intellectual property generation
• Co-marketing and amplification of joint solutions
• Business, technical, sales, and marketing resources to help you
grow your business

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KEY TAKEAWAYS

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Key Takeaways
• Most existing IoT data is rarely used for prediction & optimization
• Huge explosion in number of connected devices
• Edge Computing will be the key to generate value from data driven
insights by the ability to act independent and real-time
• Key to enable Augmented/Virtual Reality applications where they are
needed – close to physical assets

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Thank You!
• jwaltl@amazon.com
• jzal@amazon.com

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THANK YOU!
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