Installation

The Arduino IDE needs to have the ThingSpeak library installed in order for your devices to know how to send data to ThingSpeak. In the Arduino IDE, choose Sketch, Include Library, and Manage Libraries. Search for “thingspeak” and click Install.

Device Support

The ThingSpeak Communication Library supports many devices. Using the library makes the experience the same for each board type. When you learn one way to work with ThingSpeak, you will be to work with other devices in the same way.

• Arduino or compatible using a WiFi Shield
• Arduino or compatible using a WiFi Shield 101
• Arduino or compatible using an Ethernet Shield
• Arduino or compatible using a MKR ETH Shield
• Arduino MKR1000 (use the WiFi101 library version 0.13.0 or older. WiFi101 library versions 0.14.0 and newer have a bug that stops this ThingSpeak library from working properly)
• Arduino MKR1010
• Arduino VIDOR 4000
• Arduino GSM 14000
• Arduino Yún (Rev1 and Rev2)
• ESP8266 (tested with SparkFun ESP8266 Thing – Dev Board and NodeMCU 1.0 module)
• ESP32 (tested with SparkFun ESP32 Thing)

Each supported device includes three ThingSpeak examples.

• ReadField: Reading from a public channel and a private channel on ThingSpeak
• WriteSingleField: Writing a sensor value to a single field on ThingSpeak
• WriteMultipleFields: Writing sensor values to multiple fields and status in one transaction with ThingSpeak

Request Status Codes

To get the best compatibility with ThingSpeak IoT services, we recommend using the ThingSpeak library. The library has extra features that help you troubleshoot and get visibility into any issues with requests to ThingSpeak. I find it useful to store the last status code from ThingSpeak. I can use this code to understand if the request succeeded or failed. Here’s an example of how to use the “getLastReadStatus” method.

 // Read in field 1 of the private channel which is a counter  
 long fieldValue = ThingSpeak.readLongField(myChannelNumber, myFieldNumber, myThingSpeakReadAPIKey);  

  // Check the status of the read operation to see if it was successful
 statusCode = ThingSpeak.getLastReadStatus();
 if(statusCode == 200) {
   Serial.println("Field Value: " + String(fieldValue));
 }
 else {
   Serial.println("Problem reading channel. HTTP error code " + String(statusCode)); 
 }

The code behind the ThingSpeak library is available on GitHub. Discover other MathWorks Open Source and Community Projects on The MathWorks GitHub page.

On Friday, October 26, 2018, the University of Louisiana at Lafayette opened its doors to over 900 students participating in Science Day. This is a campus-wide event where high school students throughout Louisiana come to visit university’s various science departments. Our department, the College of Computing and Informatics, put on four demos for students to partake in.

The Association of Computing Machinery – Women (ACM-W) club, an organization dedicated to increasing the number of women in tech, decided we should do something different than the previous year for our demo. Our organization’s advisor, Dr. Sonya Hsu, gave us the idea to try a Deep Learning and IoT, Internet of Things demo presented at Grace Hopper Celebration of Women in Computing this past September by the team from MathWorks. With the help from the MathWorks GHC team and colleagues, we were able to put on this spectacular demo at the Science Day at the University of Louisiana. You can read about the GHC 18 Deep Learning and IoT workshop here on this blog.

We gathered all of the required tips from Anoush Najarian of MathWorks, configured all of our laptops, and put together an informal and interactive presentation. And we had our students very entertained! They really enjoyed taking photos of the fruit (and sometimes themselves) in order to see how and which objects were classified. Students kept taking photos until their fruit was correctly labeled, seeing how holding the object in a certain position or light would affect the MATLAB program’s answer.

We did notice that with smaller groups, it was easier to help everyone and keep control of the room. This made the demonstrations and feedback sessions a lot more educational: in larger groups, most people could not get most of their questions answered in the allotted time. But, the interactive segment of the demo did make up for it.

Meanwhile, the MathWorks team in Boston could keep up with what we’re doing by looking at the data collected on ThingSpeak and analyzing it with MATLAB – that’s IoT in action!

This was an amazing outreach opportunity for our organization that could not have been executed without the help of the MathWorks team! The positive feedback and help really made this an enjoyable experience for my team, and we hope to partner with MathWorks in the future.

You too are welcome to use our GHC 18 Deep Learning and IoT workshop materials, and share your thoughts in the comments! Check out the work of awesome women in engineering and science we have been highlighting with the #shelovesmatlab hashtag!

The tide height is calculated using an ultrasonic level sensor. This measurement is taken periodically and then sent to ThingSpeak, an IoT analytics cloud platform by MathWorks, using a cellular modem. The system can easily be adapted to collect data about any environmental system such as greenhouses or oyster farms.

Once you have the data in a ThingSpeak channel, you use MATLAB to preprocess and clean up the data. The raw data some times has extraneous values caused by environmental factors such as lighting, cabling, and electrical interference. Sometimes, you have missing data caused by connectivity issues. It is important to clean up the data before you use the data in your analysis.

To predict future tide levels and send alerts when the tide is rising or falling, we use the MATLAB Analysis app on ThingSpeak. With MATLAB, we can use historical data to make a prediction about the future tide levels. This predicted tide level can be used to help schedule a boating trip or plan for a water surge after a storm.

Tide Alerts

Remembering to check the tide level when fishing or lazing on the beach is not particularly convenient. A much more useful approach is to have the system send a message when the time has come to pack up and start heading back to the dock. The timing of the alert depends on how much water depth is needed by a particular boat. Larger boats need higher water levels in order to move without getting stuck in the mud. One way to send alerts is to use ThingSpeak and MATLAB to detect changes in tidal height and send alerts.

Conclusion

Developing a tide monitoring system provided accurate tide level measurement and tide level prediction, with the added ability to send alerts. Robert has been able to avoid being stuck in the bay by providing enough time to get back to his dock using this system. This project also serves as a useful approach to solving many data-driven puzzles by having a reliable way to collect, analyze, and act on data. Using MATLAB, the accuracy of the tide levels improved by understanding the proper tide levels at a specific location and when the tide levels will change. If you used the general tide forecast, you would have to account for several inches of tide height difference.

Resources

• Developing an IoT Analytics System with MATLAB, Machine Learning, and ThingSpeak – MathWorks Technical Article by Robert Mawrey
• Measure and Analyze Tide Levels with ThingSpeak and MATLAB – Hackster tutorial
• TideAlerts.com – Robert’s website dedicated to the exploration of tides

MathWorks Arduino Resources

• Arduino Support from MATLAB
• Arduino Support from Simulink
• ThingSpeak Communication Library for Arduino

Maybe it’s time to build your version of a MATLAB and Arduino powered dartboard.

We hope you build some awesome projects using Arduino, MATLAB, Simulink, and ThingSpeak on Arduino Day!

Anders was determined to measure sound from over 20 demo stations at the same time and figure out who the the winner is. This turns out to be a complicated challenge and he used our tools such as MATLAB, Simulink, and ThingSpeak, to produce some interesting results. Here’s what the raw data looks like from just five sound sensor nodes at the demo stations.

The sensor nodes uses the Arduino Nano devices because they’re small, low-cost, and Simulink has an Arduino support package. Arduino Nano’s both low cost and energy efficient which is great, but it doesn’t have Wi-Fi. They connect the sensor nodes to an Internet-connected Raspberry Pi using an RF mesh network with the nRF24l01+ radio. The RF24 solution is both very cheap and energy efficient, which is valuable if you’re running them with battery power. Simulink Coder has a Raspberry Pi Support Package which simplified the workflow.

When you follow the tutorial, you are going to learn many things and experience their analytic workflow as they decide how to develop an algorithm to normalize sound levels, determine which data to send to ThingSpeak, and build visualizations to see the results of the project.

Anders also shared a library on File Exchange that allows users to communicate with RF24 chips on Raspberry Pi and Arduino boards. The library relies on the RF24Mesh library, and has S-functions that interact with the classes there. The File Exchange submission includes an example for the Arduino to read sensor data from a temperature sensor and sends it to the gateway Raspberry Pi and then sends the data to ThingSpeak. In order to download the File Exchange, you need to sign in with your MathWorks account. This would be the same account that you use on ThingSpeak.com.

Visit ThingSpeak Tutorials, to see the complete tutorial for Building a Dynamic and Self-organizing Network of Devices.

You might have noticed recently on ThingSpeak that you can link your ThingSpeak user account to a MathWorks Account. By doing so, you have access to other MathWorks products and services that you can use with the same user account. MATLAB Mobile is a native Apple or Android app that allows you to evaluate MATLAB commands, create and edit files, view results, acquire data from sensors, and visualize data. MATLAB Mobile also has thingSpeakRead and thingSpeakWrite functions built-in. One exciting aspects of MATLAB Mobile is that you can capture the sensor data of the mobile device and send it to MATLAB Online.

To take the project further, you can use additional toolboxes from MathWorks to preprocess the data and do advanced mapping. Takuji demonstrates using the Signal Processing Toolbox to filter, down sample, and remove outliers from the incoming data from the MATLAB Mobile sensors. Using the Mapping Toolbox, he plots the latitude and longitude of his mobile device on a WPS map and displays the map on a ThingSpeak channel.

Takuji has shared all of the source code and steps on File Exchange so you can replicate this project on your own mobile device. Check out his project on File Exchange and see his raw data and visualizations on ThingSpeak.

The ThingSpeak MQTT broker is available now to all ThingSpeak users!

To help users get started using MQTT to send data to ThingSpeak, we have created some examples for common devices and applications that support MQTT.

• Use Arduino Client to Publish to a Channel – Publish data to a ThingSpeak channel using an Arduino^® device
• Use Desktop MQTT Client to Publish to a Channel – Publish to a channel using desktop MQTT client such as MQTT.fx
• Use Particle Photon Client to Publish to a Channel – Publish data to a ThingSpeak channel using a Particle Photon device
• Use WebSockets to Publish to a Channel – Publish data using WebSockets to a ThingSpeak channel

If your device or application is not able to use MQTT directly, we have also enabled WebSockets. Using MQTT over WebSockets allows devices to use the MQTT protocol to send messages behind a firewall. We also support secure sockets to encrypt the messages that are being sent to ThingSpeak.

To learn how to use MQTT with ThingSpeak, please review our documentation. Let us know what you build with this new capability.

As the ThingSpeak user community grows, we have been hearing requests for sending many millions of messages to ThingSpeak, connecting more devices, and building scalable commercial solutions. To address these requests, we are releasing new paid options for ThingSpeak. For more information, see the How to Buy page and the ThingSpeak licensing FAQ.

ThingSpeak users can continue to send up to 3 million messages per year for free (about 8200 messages per day). That satisfies the needs of 99.5% of the user community. To determine how many messages you are using, you can login and look at your account usage.

The project uses MATLAB to create a cloud-based people counter by detecting faces with the Computer Vision System Toolbox. The raw people count is then sent to the ThingSpeak IoT platform for data collection in the cloud and further data analysis.

Check out File Exchange to learn how to build your own people counter using MATLAB and ThingSpeak.

The Uber API allows you to pass a latitude and longitude to determine the estimated time of arrival for an Uber car. The API also allows you to schedule a car. I have made a button that requests an Uber car and also schedules an Uber at the right time.

MATLAB Analysis Code

% Read the ThingHTTP for 'Uber Ride Estimate'
data = webread('https://api.thingspeak.com/apps/thinghttp/send_request?api_key=XXX')

% Convert the response to a number
eta = str2num(data);

% Write the data to the 'Uber Ride Estimate Data' ThingSpeak Channel
thingSpeakWrite(Channel_ID,eta,'WriteKey','XXX');

Each time the MATLAB Analysis code is executed, it will write the estimated time of arrival (ETA) for Uber to your ThingSpeak channel. To track the ETA over time, schedule the MATLAB code with TimeControl. I am running the code every 5 minutes to get an idea of when the peak times are for Uber to pick me up at my office in Natick, MA. Check out the ThingSpeak channel number 840700 to see the estimated times.

Step-by-step project details are available at Hackster.io.

It has been a real treat watching this project evolve as more and more people add lights… and other things. Things like Android and iPhone apps that check the latest color of CheerLights, Christmas trees, and robots.

To control the lights around the world, send a Tweet mentioning @CheerLights and a color. The command is processed by the ThingSpeak IoT analytics platform and distributed to all of the lights listening to the CheerLights API.

@CheerLights I am dreaming of a White Christmas

Internet of Things

Another powerful aspect of the CheerLights project is that is shows off what is possible with the emerging Internet of Things. With a single message sent via a social network like Twitter, 1000′s of objects around the world are in sync with each other. Lights are connected by many types of controllers, such as Arduino, ioBridge, Philips, and the Raspberry Pi. This project is only possible through the Internet and the coordination of developers around the world.

In the article, “How the Internet of Things Will Change Our Lives“, CheerLights is included to indicate how we are connected and how objects may bring people closer.

Learn how to join the project at CheerLights.com.

We are all connected…

https://blogs.mathworks.com/iot/documentation/apps/tweetcontrol/

TweetControl allows you to monitor Twitter for trigger words to send ThingHTTP requests. The CheerLights project by ioBridge Labs uses TweetControl to update its ThingSpeak Channel so other lights around the world stay in sync with each other.

Why use TweetControl? Our app connects to the Twitter Streaming API. What this means to you is that you don’t have to keep polling Twitter for status updates. You can sit back and let TweetControl listen and then process the request when a trigger word gets fired. This happens in real-time and it’s quite remarkable to see in action.

TweetControl is a part of our collection of apps for social things.

Imagine an “Easy Button” for Twitter. All you have to is Tweet a hashtag from your Twitter account to control anything that has a web service API.

The applications for TweetControl are endless, and we are excited to see what you come up with. Check out the documentation for TweetControl to help you get started.

Thanks for the feature request, “nr”. Take this as an open invitation to everyone else…If you have questions, comments, or feature requests, feel free to send them in!