1. The Internet of Things (IoT) and Analytics
Class 1: The Different Things
of the IoT
March 7, 2016 Louis W. Giokas

2. This Week’s Agenda
Monday The Different Things of the IoT Tuesday A Look at Communications and Devices Wednesday Cloud Storage and Formats in the IoT Thursday Examples of Big Data Analysis Friday Machine Learning & Analysis Techniques

3. Course Description The IoT generates a vast amount of data.
This data can be used for many purposes, from product design, service and support, marketing, and control.
There are three levels of devices: the things, communications infrastructure and storage.
Tying it all together are analytic techniques.
In this course, we will build from the bottom up and then look at how the analytics infrastructure can be used in applications.

4. Today’s Agenda Course Motivation and Outline Application Areas
Consumer
Industrial
Medical
Infrastructure
Some device types
Class input

5. Course Motivation and Outline
The IoT is very diverse. This diversity extends from the types of things to their applications.
The IoT also generates unprecedented amounts of raw data.
The use of this data is similarly diverse, as are the consumers of the data. Analytics is how we make sense of the data an either present it to human consumers or communicate it amongst the things and applications that manage things.

6. Course Motivation and Outline
In statistics (or analytics) the type of analysis done is generally dependent on the data available and the “research” questions to be answered.
There are many ways to perform each analysis, in general. Requirements driven by the particular situation can determine which are applied.

7. Course Motivation and Outline
Statistical considerations are also critical. One must be able to explain more than the quantities derived. Sources of variation, confidence intervals, applicability and limitations of the analysis are also critical.
Probability theory is also key. Many machine learning algorithms will always find a “pattern”, but is this an artifact of the process or real finding?

8. Course Motivation and Outline
We will follow a Systems Engineering approach to bring this all together!
Requirements are key!
These are driven by what we want to achieve, but…
There are also considerations driven by the available devices, their measurement accuracy, update rates, etc.
In the IoT world, communications and storage are also key. We can’t analyze what we can’t collect or store.

9. Course Motivation and Outline
You may have heard about the “struggle” going on between “statistics” and “data science”.
There is also a current in engineering that posits that we don’t need models, but can get the information we need from the data. Some of these discussions have appeared in IEEE journals.
The truth is that statistics covers data science and, indeed, it is essential for real understanding.
Data engineering (storage, access and communication) are also important.
There is significant overlap here.

10. Course Motivation and Outline
Plan of the course:
Start with the things, looking at types and information they generate, as well as available methods of communication.
Communications infrastructure is a major consideration for the IoT in many areas. We will look at this in terms of specific IoT implementations and equipment.

11. Course Motivation and Outline
We need to learn how to store unprecedented amounts of data. This means utilizing technologies developed for the scale of the Internet.
Once we have this data and can operate on there are two orthogonal sets of considerations.
Purpose of the analysis
Statistical techniques

12. Application Areas Consumer
Consumer uses span a broad range of uses and devices
Smart phones and smart watches/fitness bands
Home control
Thermostats and smart appliances
Home security systems including detectors (CO, etc.)
Entertainment

13. Application Areas Consumer
Types of data generated
Location
Orientation
Heartrate
Events (e.g., threshold exceedance)
Status
For a thermostat, temperature and presence, settings

14. Application Areas Consumer
Communication types
Fitness band: blue tooth to a cell phone to the “cloud”
Direct Internet connections
Direct cellular connection
Some are one way, some go both directions
Collection only vs. collection and control

15. Application Areas Industrial
The things span a number of applications
Plant equipment
Robots
CNC machines
Networked monitoring and control equipment
“Dispersed” machinery
Large engines (e.g., from Cummins for vehicles and power generation)
Monitoring equipment
Data Centers

16. Application Areas Industrial
Types of data generated and consumed
Engine status (periodic, e.g., 5 minutes)
Machine actions (events) and status
Program in operation
Specific movement commands

17. Application Areas Industrial
Communication types
Industrial, in plant
Industrial Ethernet/EtherCAT
Profibus
Modbus
Wireless (WiFi for industrial control)
Fieldbus
Many vendor proprietary protocols

18. Application Areas Medical
Medical devices
In care facilities
In homes
In-body (embedded)
Facility monitoring
Supplies
Devices (location and scheduling)

19. Application Areas Medical
Types of data generated and consumed
Patient data (heartrate, glucose levels)
Dose control information
Equipment location
Medication and supply level and location
Equipment health and status

20. Application Areas Medical
Communication types
Ethernet
WiFi
Cellular
Bluetooth

21. Application Areas Infrastructure
Areas of application
Electrical infrastructure (utility and private)
Water systems
Road systems
Building systems
Gas infrastructure

22. Application Areas Infrastructure
Types of data generated and consumed
Meter data
Customer premise
Flow (for water utilities)
Intermediate devices
Utility infrastructure (substations, generation, transmission equipment)
Structural status
Roads, bridges
SCADA

23. Application Areas Infrastructure
Communication types
Cellular
For dispersed meters, engines, etc.
Special data only
Ethernet over powerline
Satellite
For very remote locations

24. Some Device Types Consumer devices Industrial devices
ARM based microcontrollers from companies such as STMicro (STM32), Texas Instruments (MSP430, C2000), NXP/Freescale (Kinetis)
Industrial devices
Intel based devices
Quark
Atom
ARM based devices
Cortex R
Cortex M

25. Class Input
What types of analytics/control applications are you interested in?
Type into the chat.
I will try to address these in the last two classes
Are you doing any of this now?
Examples that can be worked into the analytic framework

26. Summary and Preview
Today we covered the requirements aspects of the IoT that might affect analytics
What are the device types?
What data do they generate?
How do they communicate?
Tomorrow we will build on this with a look at how the devices communicate to create a “system”.