30% of global food produce is wasted in the supply chain: storage, movement, and delivery. By using AWS IOT to enable sensors to manage the supply chain and big data to understand patterns, industrial companies can gain efficiencies in electricity and transportation.

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Jason Massey, John Crawford, Asif Khan
November 29, 2016
IOT 402
IoT Blueprints: Optimizing Supply for Smart Agriculture
from the Farm to the Table

2. What to expect from this session
Deep dive into how we scale alerts using AWS IOT
Introduction to
IOT architectures overview

3. Our story starts with the most
fundamental question in any
industry

4. How to do more with less?
Increase production
AND
Reduce waste

6. E=mc2

8. X 30

9. 30 billion lbs
a year

10. 10^9
chicken
feet

11. 4 million lbs a day
All of the strawberries
consumed each year
are produced in just
12 weeks

12. A race against time to bring
together siloed data and create
operational context

13. The new
"iOS"

14. What a modern industrial OS looks like
Apps
SDKs
(Data stacks) Files
Rate
schedules
Weather
Facilities
& cost
centers
Real-time
data
Alerts
Custom app
Lineage app

15. Admin/monitoring apps
APIs
Stream processing/analytics
Workers
Code that brings in data, organizes data, or does any other dirty work
Processes, transforms, stores data from streams or batches
Exposes an interface for apps to consume data that the bottom two layers have put in our
stack database
Presents an interface for users to administer, monitor, and visualize data in this stack
Database(s)
Blueprint of a data stack
(SDK)

16. Lambda
RDS (Postgres)
Events API
Alerts admin
app
AWS
IOT
SNS
Workers
Stream processing/analytics
APIs
Admin/monitoring apps
Blueprint of a data stack
(SDK)
Database(s)

17. What is a blast cycle??
TL;DR: Freeze something as fast as possible
Very energy-intensive process
The faster it can be frozen, the better the
quality of the product
What it looks like
Optimizing blast cycles
What the heck is a blast cycle?

18. What is the strategy?
When the energy rate for the next rate interval is above
$0.50 / kWh, I want to determine whether to shut down
I want to base this decision on the product I’m freezing
I want to try to determine how much time I have left in the
process to help my decision-making process
Optimizing blast cycles
Strategy

19. Texts/
phone
notifications
Bunch of
sensor data
AWS
IOT
Events
API
SNS
Alert
streams
Rule registration
User
subscriptions
Lambda
functions
Rate
schedule
stack
Hourly
rate
emitter
Amazon
ML
Real-time
data stack
Bunch of
rate
schedules
Optimizing blast cycles
Architecture overview
Rule
triggers
Device
data
stream

20. Texts/
phone
notifications
Bunch of
sensor data
AWS
IOT
Events
API
SNS
Alert
streams
Rule registration
User
subscriptions
Lambda
functions
Rate
schedule
stack
Hourly
rate
emitter
Amazon
ML
Real-time
data stack
Bunch of
rate
schedules
Optimizing blast cycles
Alert registration
Rule
triggers
Device
data
stream

21. Events
API
Alert #1
Based on previous analytics of blast cycles, we’ve
determined the optimal blast cycle time should be 48 hours
for 300,000 lbs of California squid.
SELECT *,'36' as alert_id
FROM ‘things/devices/3027'
WHERE timestamp> 1473177600
Optimizing blast cycles
Alert registration

22. Events
API
Alert #2
We want to take into consideration when the electrical rate is
more than 50 cents per kWh so we can determine if we want
to pause the cycle.
SELECT *,'36' as alert_id
FROM ‘things/rates/56’
WHERE rate>0.50
Optimizing blast cycles
Alert registration

23. Texts /
Phone
Notifications
Bunch of
sensor data
AWS
IOT
Events
API
SNS
Alert
Streams
Rule registration
User
Subscriptions
Lambda
Functions
Rate
schedule
stack
Hourly
rate
emitter
Amazon
ML
Real-time
data stack
Bunch of
rate
schedules
Optimizing blast cycles
Alert registration
Rule
TriggersDevice
data
stream

24. Rate
schedule
stack
Real-time
data stack
Optimizing blast cycles
Overcoming interval disparity
Rate-change frequencies
Never (flat pricing)
Hourly (real-time pricing)
Less frequently (4 times per day,
predictable rates)
Sensor-reporting frequencies
Variable
Per-second to per-hour
usually
Sent to AWS IOT by tapping into queue and
pushing data over MQTT
Worker sends rates for next hour every hour
to AWS IOT over MQTT
Streaming
Worker push
(hourly)

25. Rate
schedule
stack
Optimizing blast cycles
Overcoming interval disparity (using device shadows)
Worker push
(hourly)
Rules
engine
things/rat
es/56
things/rat
es/57
Device shadows
.
.
.
things/rates/56
things/rates/57

26. Rate
schedule
stack
Optimizing blast cycles
Rate schedule message payload
Worker push (hourly)
things/rates/56
{
“rate_id”: “56”,
“rate_start”: “1474632000”,
“rate_end”: “1474635600”,
“period_id”: “42453”,
“rate”: “0.75”,
“units”: “dollars”
}
To AWS IOT

27. Optimizing blast cycles
IoT device message payload
One topic stream per device
things/devices/3027
To AWS IOT
Real-time
data stack
{
“device_id”: “3027”,
“timestamp”: “1474657920”,
“field_name”: “BlastCellEnable”,
“value”: “1”,
“units”: “Boolean”
}

28. Bunch of
sensor data
Events
API
Rule
registration
Rate
schedule
stack
Real-time
data stack
Bunch of
rate
schedules
Optimizing blast cycles
What’s happening inside AWS IOT
Device
data
stream
Rules
engine
things/devices/3027
things/devices/3028
things/devices/3029
things/devices/3030
things/rates/56
things/rates/57 Rule
trigger
Lambda
0.75 > 0.50

29. Texts/
phone
notifications
Bunch of
sensor data
AWS
IOT
Events
API
SNS
Alert
streams
Rule registration
User
subscriptions
Lambda
functions
Rate
schedule
stack
Hourly
rate
emitter
Amazon
ML
Real-time
data stack
Bunch of
rate
schedules
Optimizing blast cycles
Rule triggers
Rule
triggers
Device
data
stream

30. Rate
schedule
stack
Optimizing blast cycles
High rate detected
things/rates/56
Lambda
0.75 > 0.50
1. Ask the Events API for more
information about the alert.
2. Gather some timing information
from the blast cycle data.
3. Gather some information about
what’s in the blast cell.
4. Send data off to Amazon ML for
analysis (to trigger or not to
trigger?)
5. Trigger (or don’t trigger) an alert.
Function for handling a high rate
Rule
trigger

31. Events
API
Rate
schedule
stack
Optimizing blast cycles
Communicate with Events API
things/rates/56
Lambda
0.75 > 0.50
1. Ask the Events API for more
information about the alert.
Rule
trigger
Determine any dependencies for this
alert.
1. This alert also depends on a
timer for a specific blast cell.
2. Alerts API provides additional
info about the blast cell —
references to production system
about its location.

32. Rate
schedule
stack
Optimizing blast cycles
Get blast cell info from device shadow
things/rates/56
Lambda
0.75 > 0.50
2. Gather some timing information from
the blast cycle data.
Rule
trigger
Grab info from blast cell’s device
shadow (3027).
1. Is this blast cell still on? (If not,
we don’t need to do anything.)
2. How long has it been on?
things/devi
ces/3027
On for 38.5 hours

33. Rate
schedule
stack
Optimizing blast cycles
Retrieve production information
things/rates/56
0.75 > 0.50
Talk with the Production API in the
OpsMetric (warehouse management
system) data stack.
Using the information retrieved
through the Events API, query the
WMS system to see what’s in the
blast cell.
3. Gather some information about what’s in
the blast cell.
Rule
trigger
Production
API
Lambda
1,500 cases of California squid
in spaced racks

34. Rate
schedule
stack
Optimizing blast cycles
Send information to Lambda
things/rates/56
0.75 > 0.50
4. Send data off to Amazon ML for analysis
(to trigger or not to trigger an alert?)
Rule
trigger
Amazon
ML
Lambda
• Rate = $0.75 / kWh
• On for 38.5 hours
• 1,500 cases of California
Squid in spaced racks
Information to send:

35. Rate
schedule
stack
Optimizing blast cycles
Trigger an alert
Lambda
5. Trigger (or not) an alert.
Depending on what Amazon ML told
us to do, we either:
1. Trigger an alert to shut down
the blast cell.
2. Ignore it.
Trigger!
things/rates/56
0.75 > 0.50
Rule
Trigger

36. Texts/
phone
notifications
Bunch of
sensor data
AWS
IOT
Events
API
SNS
Alert
streams
Rule registration
User
subscriptions
Lambda
functions
Rate
schedule
stack
Hourly
rate
emitter
Amazon
ML
Real-time
data stack
Bunch of
rate
schedules
Optimizing blast cycles
SNS as alert mechanism
Rule
triggers
Device
data
stream

37. Texts/phone
notifications
SNS
Alert
streams
Optimizing blast cycles
SNS as alert mechanism
facility_65_stream
Lambda
alert_36_stream
Texts/phone
notifications

38. Optimizing blast cycles
Result
Shut down blast cell during this time for
5 hours
1. Save signification amount of
money and energy.
2. Still preserve quality of food.

39. Using Amazon Machine Learning for advance prediction of energy consumption
of a plant or a facility
Using Amazon Machine Learning for equipment health monitoring for risk
management
Integrating transportation data with AWS IoT
Optimizing blast cycles
Next steps

40. IoT architectures look and feel
different…

41. Things are NOT static assets

44. The customer has many identities

47. The home is someone’s enterprise

49. IoT architectures look and feel different…
IoT architectures:
• Intermittent network
• Devices are geo-dispersed
• Stateless transactions
• No IP
Web architectures:
• Reliable network
• Servers aren’t mobile
• Stateful transactions
• IP address

50. The “Internet of Things” (plural)

51. AWS IoT

52. Thank you!

53. Remember to complete
your evaluations!