At WomenWhoCode, 2019 https://india.womenwhocode.dev/agenda/ Edge Computing: What, Why, How and Where Edge Analytics: What, Why, benefits, limitations Edge computing vs Edge Analytics Edge Analytics use-cases

1. Introduction to Edge Analytics
Intelligent IoT
WWCode CONNECT 2019
29th March, Bangalore, India
https://india.womenwhocode.dev/agenda/
Shreya Mukhopadhyay
Senior Technical Program Manager
Intuit

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IoT: Cloud computing
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Includes
- 2000 IoT sensors
- Smart meters
For What?
- Predicting demands
- Improved Energy and Water
management
Link
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Cloud
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Agenda
➔ Edge Computing: What, Why, How and Where
➔ Edge Analytics: What, Why, benefits, limitations
➔ Edge computing vs Edge Analytics
➔ Edge Analytics use-cases
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Oil Rigs: Monitoring device data
Cloud
Oil rigs
Oil company city office
➔ Production planning
➔ Maintenance needs
➔ New drilling sites
➔ Environmental
conditions
Valve failure, fire- needs
immediate action (low TTR)
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ATM monitoring devices
Cloud
ATM’s
Bank Headquarters
➔ Improving user
experience
➔ Transaction patterns
➔ Maintenance schedule
Fraud- needs immediate
action ( low TTR)
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Cloud computing limitations
➔ Speed - Higher time to react (TTR)
➔ Real-time Decision-making and actuation delayed
➔ Connectivity issues
➔ Privacy concerns
Gartner states 90% of the data created is unusable, 32% is inaccurate
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Edge Computing
Edge computing is the process of offloading compute and storage from the
centralized Cloud to the network’s logical extremes
➔ Provides ultra-low latency ⚡
➔ Eases the network bottleneck
➔ Supports local data processing
➔ Enables previously impossible applications
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Computing Layers
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Fog Computing (termed by Cisco) Edge Computing
- Nodes, mini-cloud
- Compute and storage- Local network
assets, micro-data centres
- Insights from mini cloud
- Device
- Compute and storage resides at the
edge level, close to the device
- Insights directly from device
Edge Platforms- Microsoft Azure IoT Edge, Lambda@Edge, Dell’s Edge Gateways,
VMware's Pulse IoT Center, IBM Watson IoT Platform
e.g. Connected hospital, Surveillance e.g. Connected valves, sensors
Layers: Edge and Fog
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Edge Analytics
Edge analytics is an automated analytical computation of data at a sensor,
network switch or other device level
➔ Analyzing data as it's generated, near to the source
➔ Decreases latency in the decision-making process
➔ Powers scalability, subset of data sent to cloud for storage and to power Big
Data Analytics
Platform- IBM's IoT analytics, AWS IoT Analytics, SAS
Models from cloud to edge- PMML
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Home: Water use info and data
Cloud
Smart Water Meter
City Water department:
Water use info and data
Computing
➔ Collect data, log, track
➔ Send data to cloud for
storage, enable to train
models
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Society Water
Management
Leaks/overflow- needs
immediate action (low TTR)

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Home: Water use info and data
Cloud
Smart Water Meter
City Water department:
Water use info and data
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Society Water
Management
Analytics
➔ Deploy models trained in cloud at the edge
➔ Water meter sends alert to homeowner
➔ Meter automatically controls the valve to shut off
water supply
Leaks/overflow- needs
immediate action (low TTR)

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Edge Computing Edge Analytics
Edge computing is using devices strictly to
act as computers to
- log events
- performing inter-device
communication
- tracking location etc
Edge analytics is leveraging that same
device or devices and utilizing them to
process the data that has been computed
and turn it into actionable information
directly on that device
Perform analytical functions- what should
go to cloud, what shouldn’t?
Descriptive OR predictive OR prescriptive
analytics at the edge
Edge: Computing vs Analytics
➔ Computing and analytics are not mutually exclusive
➔ 2 sides of the same coin- one cannot exist without the other
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Analytical Requirements
Descriptive
● Analyze stored data at rest to provide overview of historical outcomes and performance
● Analyze data in-stream to provide real-time understanding of current state operations
Predictive
● Identify probabilities of potential outcomes and/or likely results of specific operations
● Provide real-time predictive indicators and likely portfolio of outcomes
Prescriptive
(Automation)
● Provide specific recommendations based on live streaming data to an employee or to
automatically initiate a process
● Contextualize live stream events within current business requirement
● Optimize future recommendations based on trained models
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Home: Water use info and data
Cloud
Smart Water Meter
City Water department:
Water use info and data
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Society Water
Management
Descriptive
➔ Month wise water
consumption patterns
➔ Plans for the next
season
Predictive
➔ Society: How many
tankers do we need to
order
➔ Water dept: Plans for
the next season
Leaks/overflow- needs
immediate action (low TTR)
Prescriptive (Automation)
➔ Water meter sends alert to
homeowner
➔ Meter automatically
controls the valve to shut
off water supply

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Edge Analytics: Benefits
- On-time decision making
- Removes attic data (Gartner states 90% of the data created is unusable, 32%
is inaccurate)
- Saves processing and uploading time
- Preserves privacy- only processed, compliant data sent to cloud
- Greater production capacity planning and prescriptive maintenance
- More efficient service and warranty processes
- Lower impact of connectivity issues
Limitation: Lost data- subset sent to the cloud
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19. Questions ??
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Contact Details:
Email: shreya_mukhopadhyay3@intuit.com
Linked-in : https://www.linkedin.com/in/shreya-mukhopadhyay/

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Appendix
Smart Water Meter:
https://smarterhomes.com/?gclid=Cj0KCQjwhPfkBRD0ARIsAAcYycFvvj6qLS1mXbqC0iW8agguMZ48t3rdnzSuepHckHZBBRgO1FfnvS
QaApEMEALw_wcB
Cloud vs Fog: https://www.winsystems.com/cloud-fog-and-edge-computing-whats-the-difference/
Edge Platforms- Microsoft Azure IoT Edge, AWS Lambda@Edge, Dell’s Edge Gateways, VMware's Pulse IoT Center, IBM Watson IoT
Platform
Edge Analytics Platform- IBM's IoT analytics, AWS IoT Analytics, SAS
Models from cloud to edge- PMML
Edge Analytics Pros, cons: https://www.rightscale.com/lp/state-of-the-cloud
https://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/The-pros-and-cons-of-IoT-edge-analytics
Other awesome reads: https://www.zdnet.com/article/10-edge-computing-vendors-to-watch/
https://www.slideshare.net/mKrishnaKumar1/open-source-edge-computing-platforms-overview?qid=559f4ab8-ed8c-4928-bbbb-2cddd
723c9e9&v=&b=&from_search=4
https://www.cmswire.com/analytics/what-is-edge-analytics/
https://www.rightscale.com/lp/state-of-the-cloud
https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/manage-analytical-life-cycle-continuous-innovation-106179.pdf
https://www.kdnuggets.com/2017/10/edge-analytics.html
https://www.sas.com/content/dam/SAS/en_us/doc/research2/iot-analytics-in-practice-107941.pdf
https://www.gartner.com/ngw/globalassets/en/information-technology/documents/insights/100-data-and-analytics-predictions.pdf
https://searchbusinessanalytics.techtarget.com/definition/edge-analytics
https://www.kdnuggets.com/2016/09/evolution-iot-edge-analytics.html
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Cloud
Surveillance Control room
- Image processing
- Encryption
- Uploading to cloud
Edge Computing vs Analytics: Surveillance
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Cloud
Surveillance Control room
- Image processing
- Encryption
- Uploading to cloud
Surveillance: Edge Computing
➔ Collect data, log, track
➔ Send data to cloud for storage,
enable to train models
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Cloud
Surveillance Control room
- Image processing
- Encryption
- Uploading to cloud
Surveillance: Edge Analytics
➔ Deploy models trained in cloud at the edge
➔ Send out alerts to hospitals, fire brigade, police
station in case of emergency
➔ Anticipate camera failures- send out maintenance
alerts
➔ Identify blind spots- move cameras in advance
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