We use data analysis and visualization capabilities of ThingSpeak, our favorite Internet of Things platform to capture and analyze performance data, to help with performance monitoring and to generate alerts

1. 1© 2016 The MathWorks, Inc.
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Performance Trends and Alerts
with ThingSpeak IoT
By Anoush Najarian • MATLAB Performance Team • MathWorks
Boston CMG • Social Media Chair at #CMGimPACt • CMG Board of Directors
@anoushnajarian

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Agenda
 The Internet of Things (IoT)
 ThingSpeak
 A few of our favorite IoT applications
 Performance Trends and Alerts on ThingSpeak

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The Internet of Things is…
The internet of things (IoT) is the internetworking of physical devices, vehicles
(also referred to as "connected devices" and "smart devices"), buildings and
other items—embedded with electronics, software, sensors, actuators, and
network connectivity that enable these objects to collect and exchange data.
https://en.wikipedia.org/wiki/Internet_of_things

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Things…
 Typical “thing”
– A sensor with connectivity
– An actuator with connectivity
 They are constrained
– Processing
– Memory
– Protocols
– Clock
– Power
 They run code over and over – sometimes they sleep wake up and run
code, and go back to sleep
 There are lots of them

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What is the Internet of Things?
Edge Nodes Exploratory Analysis
Analytic IoT Platform
Deploy analytics
to aggregator
Deploy algorithms to nodes/devices

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Agenda
 The Internet of Things (IoT)
 ThingSpeak
 A few of our favorite IoT applications
 Performance Trends and Alerts on ThingSpeak

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What is ThingSpeak?
 Analytic IoT platform
– Collect data from sensors, “things”
– Visualize data instantly
– Has more than 60,000 users
 Analyze data
– MATLAB integration allows users to run
scheduled code on data coming into
ThingSpeak
 Act on data
– E.g. send a tweet when the temperature in
your backyard reaches 32 degrees

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Who is ThingSpeak for?
 Makers
 Academics
 Engineers and scientists
https://thingspeak.com/

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ThingSpeak: Collecting Data using Channels
 For any new data, first login and
create a channel in ThingSpeak
 Channels have read and write API
keys and can be public or private
 A channel is made up of 8 fields and
can store 8 streams of data (Temp,
Humidity, etc.)
 Channels can be updated at a
maximum rate of once every 15
seconds
ThingSpeak Weather Channel

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Agenda
 The Internet of Things (IoT)
 ThingSpeak
 A few of our favorite IoT applications
 Performance Trends and Alerts on ThingSpeak

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Objectives
 Measure, explore, discover weather patterns
 Provide niche weather service
Solution
 Arduino station with weather sensors
 Cloud-based aggregation and analysis
 Full example available at
makerzone.mathworks.com
Example 1: Monitoring Weather

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Fun with Arduino!
 Arduino Serial Monitor
records the data from
the temperature sensor,
the pressure sensor,
and the humitidity
sensor, as transmitted
to ThingSpeak!
 Placing ice on
the Arduino temperature
sensor to cool it off!

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Fun with Raspberry Pi
 Building a circuit with a
temperature sensor in this
week's
 Posting the temperature
readings from the sensor
to ThingSpeak using
Twitter.

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Objectives
 Measure, explore, discover traffic patterns
 Provide live local traffic information service
Solution
 RaspberryPi + webcam
 Automated deployment of vision
algorithms on embedded sensor
 Full example available at
makerzone.mathworks.com
Example 2: Monitoring Traffic

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Smart Agriculture
 Vineyards
 Green Houses
 Golf Courses
 Weather Stations – drought studies, radiation analysis
 Compost – Fungus prevention

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Agenda
 The Internet of Things (IoT)
 ThingSpeak
 A few of our favorite IoT applications
 Performance Trends and Alerts on ThingSpeak

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Case Study: Performance Optimization of MATLAB Code
 A customer ran into slow performance issues with her code in MATLAB.
She saw such slow performance in that she decided to recode her
algorithm in another language. We wanted to show her some simple
techniques in MATLAB that could bring her code down to a more
reasonable running time.

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What is the problem the user was trying to solve?
 The code generates locations on a 2D
grid with dimensions nx1 by nx2
 The code iterates through all possible
combinations of these initial and final
positions

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The core of the code

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Measure performance of the original code

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Set up ThingSpeak channel

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Post performance data to our ThingSpeak channel

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Focusing in on the Performance Improvements
February 1 – baseline at 23.8s
 If we hover over the data points in the trend plots, or zoom in, we will spot the key
optimizations that helped performance of Sarah's code. Initial Code measured at around
23.8s:


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Performance Optimizations We Used
 Preallocation
 Vectorization
 Elimination of repeated calculations

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Optimization #1: Preallocation
 Listen to Code Analyzer!

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Focusing in on the Performance Improvements
Optimization #1: February 20 – 22.3s
 On February 20, we switched to Code with Preallocation, speeding up to
around 22.3s, or by 6%:

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Optimization #2
Vectorize the inner two loops

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Focusing in on the Performance Improvements
Optimization #2: May 20 - down to 0.2s!
 On May 2, we implemented the optimization to Vectorize the Inner Two loops; this sped up
the code 100+-fold, to 0.2s.

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Optimization #3
Vectorize the inner three loops

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Focusing in on the Performance Improvements
Optimization #3: June 9 – dipped down to 0.06s!
 Finally, on June 9, we implemented Vectorize the Inner Three Loops, and the code sped up
to 0.06s, or by 67%.

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Final Optimization, #4: Eliminate redundant work

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 In all, our trends capture pretty awesome 400+-fold performance improvement in the past
few months!
The result: 400x as fast overall!

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What’s Next?
 Use Analysis and Visualization tools on ThingSpeak for more advanced
data analysis like displaying error bars, normalizing performance across a
suite of tests
 Set up email alerts in response to changes in performance
 Use machine learning for anomaly detection and to generate smart alerts

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Thank you!
 Many thanks to the ThingSpeak team for help with these slides and for
creating and growing ThingSpeak!
 A big thank you to Andy Campbell for helping me publish a MATLAB Central
blog post on this
@anoushnajarian
Anoush.Najarian@mathworks.com
linkedin.com/in/anoushnajarian