How Can Edge Computing and IoT Transform Your Business?

1 © 2018, Amazon Web Services, Inc. All rights reserved..
How can edge computing
and IoT transform your
business?
April 19, 2018
AWS Webinar
Presented by:
Andrew Hood
IoT Business Development Manager
Amir Majlesi
IoT Business Development Manager

2 © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What we’ll cover today
• What customers are doing with AWS IoT
• Business outcomes with IoT
• Challenges of IoT deployments
• Our concept of IoT
• Value of processing data at the edge
• AWS Greengrass
• Machine Learning at the edge
• Customer examples
• Q&A

If you knew the state of everything
and could reason on top of that data…
what problems would you solve?

Predictive maintenance Wellness &
health solutions
Connected buildings
& city systems
Maintain
device fleets
Monitor energy
efficiency
Safeguard manufacturing
facilities
What customers are doing with AWS IoT
IoT payment &
connected commerce
Remote patient monitor

Revenue growth
IoT data drives business
growth
Operational efficiency
IoT data decreases OpEx
Business outcomes with IoT
New services &
business models
Products that get
better with time
Better relationship
with customers
Increased
efficiency
Intelligent decision
making
Data driven
discipline

Connect,
communicate,
secure
IoT solutions are complex & multidimensional
Onboard,
provision,
manage
Devices &
sensors
Connectivity &
infrastructure
Analyze,
visualize,
act
Analytics
& insights
Engage,
empower,
delight
Applications
& services
Transform,
shift culture
Change
management

Our concept of IoT
Things
Sense & Act
Cloud
Storage & Compute
Intelligence
Insights & Logic →
Action

AWS IoT Architecture
Things
Sense & Interact
Cloud
Store, Analyze & Act
Secure device
connectivity
and messaging

Endpoints
Fleet
onboarding,
management
and SW updates
Fleet
audit and
protection
IoT data
analytics and
intelligence
Gateway
Things
Sense & Interact
Cloud
Secure local
triggers, actions,
and data sync
Secure device
connectivity
and messaging

Secure device
connectivity
and messaging
Endpoints
Fleet
onboarding,
management
and SW updates
Fleet
audit and
protection
IoT data
analytics and
intelligence
Things
Sense & Interact
Cloud
Secure local
triggers, actions,
and data sync
AWS IoT Core
Gateway
AWS
Greengrass AWS IoT Device
Management
AWS IoT
Device
Defender
a:FreeRTOS
a:FreeRTOS
AWS IoT
Analytics

How can I extend
AWS intelligence
to the edge?

Value of processing data at the edge
Edge Cloud
Law of Economics
Law of Physics
Law of the Land

Extend AWS IoT to the Edge, act locally on the data generated
at the edge, while still taking advantage of the cloud
AWS Greengrass
Data and
State Sync
Security Over the
Air Updates
Protocol
Adapters
Local
Device Shadows
Local
Actions
Lambda
Functions
Local
Messages
and Triggers
Local
Message Broker
AWS-grade
security
Easily Update
Greengrass Core
Machine
Learning
Inference
Local Execution
of ML Models
Local
Resource
Access
Lambdas Interact
With Peripherals
Easy Integrations
With Local
Protocols
ʥ
A

Respond quickly
to local events
Operate
offline
Simplified device
programming
Reduce the cost of
IoT applications
AWS-grade
security
Benefits of AWS Greengrass

How can I enable
Machine Learning
at the edge?

What can you do with ML at the Edge?
Voice/sound
recognition
Network
Intrusion
Detection
Image
recognition
Anomaly
detection
More
!
Smart
Agriculture
Predictive
maintenance
Self-driving
cars
Video
surveillance
Robotics

Intelligence on devices
Sense
Generate and receive rich data
about the environment
Infer
Extract relevance from huge
amounts of data in real time
Action
Take smart actions

Why is it hard?
Collect and
moving data to
the cloud
Process data,
build and train
your model
Deploy model to
the target device
Build ML
framework for
different devices
Write Inference
app and deploy it
to the target
device
Utilize
accelerators
such as GPU

Build & train ML
models in the cloud
Use Greengrass to
deploy optimized models
on your device
Accelerate ML
inference applications
on the edge
Devices take action
quickly – even when
disconnected
Extend AWS IoT to the edge
AWS Greengrass ML Inference

Inference Training
Machine Learning at the Edge
Local
actions
Edge Cloud

Deploy cloud trained
models
Enable
GPU access
Use pre-build
MXNet, or bring your
own ML framework
Lambda
actions
Benefits of Greengrass ML Inference

AWS DeepLens
Deep Learning video camera
using AWS AI and AWS Greengrass
on Intel Atom (Apollo Lake)

Problem
Nokia has seen a need in industrial IoT to
analyze video streams at the edge and send
the data to remote centers only when
anomalies are detected.
Solution
Deploying Greengrass on Nokia Multi-access
Edge Computing platform and combining
it with Nokia private mobile network
solutions. This joint solution will make
it possible for the oil industry to pair real time
drilling data with production data of nearby
wells.
Impact
Due to the cost of bandwidth being
expensive, this allows Nokia to optimize the
data that is sent to other wells and to the
cloud based on rules and alerts set up on the
locally-processed data.

Problem
Stanley Black & Decker finds it unsustainable
to ingest, transmit, store, query and analyze all
data generated at the edge and more
specifically on construction sites or rural areas
with constrained network resources.
Solution
AWS Greengrass enables Stanley Black &
Decker to monitor and filter data at the edge
of the network enabling applications to send
asset health and predict any mechanical
failures before they occur. Edge-based
applications built on Greengrass will help
detect and compare vibrations emitted by high
value tools to historical signatures that indicate
everything from normal operations to
imminent failure.
Impact
Instead of trying to use all the data Stanley
Black & Decker will utilize Greengrass to focus
on the right data. Applications include remote
troubleshooting of hydraulic assets by
technicians, maintenance interval tracking, fuel
savings, and alerts.

Problem
Pentair provides water filtration systems
equipped with sensors to fish farms and large
industrial brewing customers. Most of their
industrial customers are located in
geographies with unreliable internet
connectivity. They need to send data from
sensors and devices to the cloud while
continuously maintaining connection which is
challenging in remote areas.
Solution
From Pentair’s water filtration systems, data is
sent to AWS IoT Core. When connectivity is
limited, AWS Greengrass provides Pentair with
a local connection so data is never lost.
Impact
Pentair can make decisions in near real-time
that impact the health of its devices but also
the health of the fish which in turn, results in
better yields, prevents the spread of disease
and lowers cost of operations.

Thank You

How Can Edge Computing and IoT Transform Your Business?

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