Machine Learning and Analytics in the Cloud on IoT Data

1 Copyright© 2013, Intel Corporation. All rights reserved.
*Other brands and names are the property of their respective owners.1
Machine Learning and Analytics
in the Cloud on IoT Data
Ramesh Peri
Sr. Principal Engineer
Intel Corporation
Austin, TX 78746

*Other brands and names are the property of their respective owners.
• A typical IoT System
• Characteristics of IoT Data & Machine
Learning techniques
• Intel IoTDevkit Overview
• A Complete end-to-end IoT Solution
Agenda

IoT System

A Real IoT System
Solar panels Power Mains
Envoy gateway
from enphase
Wifi router and
cable modem
Austin energy
power meter
Enphase mobile app
Enphase website
Austin energy website

Learning techniques
Agenda

Characteristics of IoT Data
Streaming Time Series
Data
Inference results in some
real-time actuation
Data is Correlated
Lack of training data or
Highly Imbalanced data

Machine Learning Techniques for IoT Data
• Rule Based SystemsRB Systems
• One class Support Vector MachinesOCSVM
• Probabilistic Exponentially Weighted
Moving AveragePEWMA
Markov Models
….
• Used to predict sequences of events
based on historical data

Learning techniques
• Intel® IoTDevkit Overview
Agenda

Intel® IoT Platforms in Market today
Name Processor/SoC Target Classification
Galileo (X1000) Quark Maker SBC
Edison Atom Maker SBC
D1000 x86 IoT Edge MCU
D2000 x86 IoT Edge MCU
C1000 Curie (x86 + Arc) IoT Edge MCU++
Arduino101 Curie (x86 + Arc) Maker MCU++
Minnow Board Atom Reference SBC
Joule Atom Maker++/IoT SBC
Gateways from PC OEMs Atom IoT Gateway Gateway
Definitions
SBC – Single Board Computer with exposed I/Os
MCU – MicroController
MCU++ - MicroController but with more stuff
Gateway – small form factor PC with no exposed I/Os

• Intel® System Studio IoT Edition
• Intel ® XDK IoT Edition
• Wind River* Helix* Cloud
• MCU development tools
• C, C++, JavaScript, Python, and Java
programming language support
Intel® IoTDevKit Overview
Supports Intel® IoT Gateways, Compute Modules,
and MCUs with Middleware libraries, tools, and
cloud connectors
IoT Cloud and Edge Analytics for data
collection, visualization

Intel® IoTDevkit Goals
Good OOB experience
Support for Sensors/Peripherals
Uniform Dev Environment
Robust Developer Ecosystem
Cloud Support

Intel® IoTDevkits
https://software.intel.com/en-us/iot/hardware/edison/dev-kit
https://software.intel.com/en-us/iot/home
Intel® NUC kit
Thin Canyon
Arduino
101
Grove
Sensor
Kit

mraa.io & UPM
API providing low-level and high-level wrappers to low-
level communication protocols development platforms
supporting many OSes
UPM is a software suite supporting over 250 sensors and
actuators http://upm.mraa.io

Intel® IoT Developer Kit IDEs
Intel® XDK IoT Edition
(Node.js applications)
Intel® System Studio IoT Edition
(C, C++, Java applications)

Robust Developer Eco System
Intel® IoT Developer Kit, with easy to use hardware, software, tools, and
cloud services.
IoT Roadshows, Hackathons, and Workshops providing hands-on, F2F
training and practical experience.
Online communities, for learning, building, and sharing.
● Intel® Developer Zone for IoT
● Partner and 3rd party communities
ISV Enabling to build an ecosystem around the IoT Platform.
Intel® IoT Developer Program is a comprehensive program for makers, hobbyists,
as well as professional developers, offering tools, kits, knowledge, and a
community of experts to help you quickly and easily turn your innovative ideas
into commercial IoT solutions.
Dream it, Build it with Intel® IoT

Cloud Services on Intel® IoT Platforms
• Cloud Connector Recipes & Package (RPM)
• Cloud enabled kits for good OOB experience
• https://www.seeedstudio.com/Grove-IoT-
Commercial-Gateway-Kit-p-2724.html
• https://www.seeedstudio.com/Grove-IoT-
Developer-Kit-Microsoft-Azure-Edition-p-
2631.html?ref=newInBazaar
• https://www.amazon.com/dp/B0168KU5FK
• Tools integration
• Sample Projects
Cloud Services for Intel® IoT Platforms include the following:

Learning techniques
Agenda

A Complete end-to-end IoT Solution
Develop a setup where temperature sensor data is plotted in the
cloud and when the temperature goes above a certain value
send out an email

The Architecture
Temp Sensor Edison
ATT Cloud
Sensor physically connected to Edison Analog port
• Time series
sensor datam2x
• Node-red
based flowsflow

On Intel ® Edison

Setup the cloud service
• Open an account at developer.att.com
• This will create accounts for you at
• m2x.att.com (a time series database service)
• flow.att.com (a cloud service programmable in node-red visual
language)
• Setup m2x
• Edison device in m2x.att.com
• Add a stream to it called Temperature
• Make a note of deviceid and apikey

Node Flow on Flow Designer From previous slide

Spin up the service in flow Pressing this will spin up a machine in
cloud with appropriate services
After a few mins (some times it may take longer) a
machine instance is created for you at bottom you will
see the details
This goes into the node-red program
on Edison in node send-to-cloud

View in m2x after data gets streamed into cloud

Add a trigger in m2x to detect hightemp condition
A simple rule based system using
a threshold value

Flow to handle the Trigger from m2x

Summary
New opportunities for
Machine Learning at Cloud and Edge of IoT Systems
Collect and
send to cloud
Massage the
data in Cloud
Send to
storage
Apply ML
Model to
detect
anomaly and
notify cloud
Cloud takes
needed
action

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