If you have used ThingSpeak MQTT in the past, I wanted to mention one big change to how you access the service. The new MQTT service is available at the hostname: mqtt3.thingspeak.com.

If you decide that MQTT is right for your IoT project, you can start by adding a new device to your ThingSpeak account. This will set up the MQTT credentials needed for the device to connect to ThingSpeak. MQTT works well for low-power devices and low-latency applications.

ThingSpeak’s new MQTT support includes:

• Improved access control
• More concurrent subscriptions
• Device management
• Streamlined topic patterns to make coding easier

We’ve updated our documentation to include several new MQTT examples with code for the new interface. We have added a new example to secure the transmission of data between devices and ThingSpeak.

• Publish and Subscribe to a ThingSpeak Channel Using Secure MQTT
• Publish and Subscribe to a Channel Using Desktop MQTT Client
• Publish to a ThingSpeak Channel Using a Particle Device Client via MQTT

What is “healthy” air quality?

Good or moderate air quality is when the Air Quality Index (AQI) is 100 or less. AQI is a relative measurement of five common air pollutants: ground-level ozone, particle pollution, carbon monoxide, sulfur dioxide, and nitrogen dioxide. A high AQI indicates a higher level of pollution and is considered unhealthy over 100.

Air Quality Sensors

We have installed a PurpleAir sensor at the MathWorks Apple Hill campus in Natick, MA. PurpleAir sensors use laser particle counters that provide an accurate and low-cost way to measure smoke, dust, and other particulate in the air. The data from our air quality sensor is available on a public ThingSpeak channel.

PurpleAir produces an interactive map using the air quality data from their sensors deployed around the world. Among other applications, the data is used by researchers to understand the effects of forest fires on air quality.

Air Quality Analysis using MATLAB

On File Exchange, we have posted our MATLAB functions used to analyze the air quality data collected by ThingSpeak. The MATLAB code helps preprocess the sensor data, provides functions to classify the data, and provides functions for visualizing the processed air quality data. The MATLAB visualizations are added to a ThingSpeak channel dashboard so you can see the current air quality near you. The MATLAB analysis code calculates the AQI using the definitions from the United States Environmental Protection Agency (EPA).

You can build a data analysis dashboard on ThingSpeak using publicly available data and MATLAB. This project it makes it easy to explore IoT data analysis using MATLAB to preprocess and visualize data without having IoT hardware.

Download the code from File Exchange and follow along with our video tutorial.

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!

ThingSpeak is a MATLAB enabled IoT analytics platform from MathWorks, the leading developer of technical computing software for engineers and scientists. With ThingSpeak, users can view instant visualizations of live data from any Internet-connected web browser and schedule MATLAB code to run live analyses and visualizations as new data arrives. To accelerate the development of IoT analytics, MATLAB offers a full set of statistics and machine learning functionality, plus advanced methods such as nonlinear optimization, system identification, and thousands of prebuilt algorithms for signal and image processing.

Libelium sensors are used in a variety of vertical IoT applications like air quality monitoring and smart agriculture that are also common applications for users of the ThingSpeak platform. “The new integration between the Meshlium IoT Gateway and ThingSpeak will allow our customers with mutual interests to quickly analyze their data in the cloud with MATLAB,” said Eric Wetjen, Senior Product Marketing Manager for ThingSpeak.

The ThingSpeak integration allows you to easily connect your Libelium devices to the ThingSpeak IoT analytics platform by using the ThingSpeak cloud connector, which is built into the Libelium Meshlium IoT gateway. The ThingSpeak cloud connector inside of the Meshlium Manager System creates the ThingSpeak channels needed for your devices and synchronizes the data automatically without writing any custom code.

Check out the MathWorks Hardware Catalog for more information about the Libelium support for ThingSpeak and MATLAB.

Here is a quick list of features of the Predictive Maintenance Toolbox for MATLAB:

• Survival, similarity, and time-series models for remaining useful life (RUL) estimation
• Time, frequency, and time-frequency domain feature extraction methods for designing condition indicators
• Organizing sensor data imported from local files, Amazon S3, Windows Azure^® Blob Storage, and Hadoop^®Distributed File System
• Organizing simulated machine data from Simulink^® models
• Examples of developing predictive maintenance algorithms for motors, gearboxes, batteries, and other machines

The Predictive Maintenance Toolbox is available on ThingSpeak to users that have a license to the toolbox. Just sign into ThingSpeak using your MathWorks Account and you will have access to the features of the Predictive Maintenance Toolbox with the MATLAB Analytics app. If you have any questions about the Predictive Maintenance Toolbox, contact Aditya Baru at MathWorks.

We also added support for tags within the ThingSpeak User API. Just pass the same tag into the API call to ThingSpeak, and you will receive a list of channels that match. This is really useful for integrating ThingSpeak into enterprise systems and for automating channel creation by deployed devices.

All of the tag-related features are available today to all ThingSpeak users!

I recently became the moderator of the MATLAB Maker Community that is hosted on MATLAB Central. There are many times where MATLAB and Simulink can help build a hardware-based project or be used to create the code running on a device. I also use MATLAB for analytics. Here are the most popular colors on CheerLights in the last 30 days.

The goal of the MATLAB Maker Community is to connect makers and builders together. I learn by working with others and sharing my work. If you are interested in maker project, I suggest following the Maker Community and jumping in on conversations or starting new discussions. I find this helpful if I am exploring a new idea or looking for feedback.

Right now, there is a discussion thread about how to use MATLAB to interface and interact with an Arduino. Makers can use MATLAB to control an Arduino by first installing the MATLAB^® Support Package for Arduino^®. Once you have the support package, you can use MATLAB to control the Arduino with familiar MATLAB commands.

% create an Arduino object
a = arduino('com3', 'uno');

% turn on an LED connected to Pin D11
writeDigitalPin(a, 'D11', 1);

% turn off an LED connected to Pin D11
writeDigitalPin(a, 'D11', 0);

Share your ideas on how to use the MATLAB connection to Arduino on the MATLAB Maker Community.

Randy Cronk, a volunteer at the MIT Enterprise Forum of Cambridge, sits down with Eric Wetjen of MathWorks and interviews him about IoT solutions from MathWorks and our ThingSpeak IoT Analytics platform. Check out the interview on the Connected Things blog.

Setting up the Sensor

This project uses the ESP-based NodeMCU as an IoT gateway to forward sensor data to ThingSpeak. The NodeMCU is essentially a microcontroller and a Wi-Fi device that costs less than $10 US. The humidity sensor that is used in this project is the DHT11. This a very common sensor used to monitor temperature and humidity. The sensor either comes in a 4 pin or 3 pin package. The NodeMCU is programmed with the Arduino IDE using the code in Douglas’ tutorial or GitHub.

Using ThingSpeak Metadata

Douglas uses the metadata setting within a ThingSpeak channel to store condition ranges. This allows you to adjust settings in your online analytics code without redeploying new code. Each ThingSpeak channel has a metadata setting. You can store arbitrary text data that can be used in your MATLAB Analysis code. To load your channel’s metadata into MATLAB, use the webread function and add metadata=true to the ThingSpeak API Read Data request.

indoorChannelData = webread(strcat('https://api.thingspeak.com/channels/', ...
                                    num2str(indoorChannelID), ...
                                    '/feeds.json?metadata=true&api_key=', ...
                                    indoorChannelReadKey));

Using MATLAB for Condition Monitoring

Douglas uses MATLAB on ThingSpeak to determine the proper condition. This is a common requirement in complex IoT systems. This example could be a good starting point for building a condition monitoring system for industrial maintenance applications. You use MATLAB to determine the target humidity using a polynomial fit over the lookup data.

lookupFit = polyfit(humidityLookup(:, 1), humidityLookup(:, 2), length(humidityLookup) - 1);
optimalHumidity = polyval(lookupFit, curTempOut);

humidityDiff = curHumidity - optimalHumidity;

Using IFTTT for Alerts

Often you want to get notifications when a certain condition is met. Douglas shows you how to use IFTTT to send push notification directly to your phone. In this project, MATLAB is determining the condition and then interfaces with the IFTTT API to send the push notification. To send push notifications via IFTTT, use the webwrite function in MATLAB.

webwrite(strcat('https://maker.ifttt.com/trigger/', makerEvent, ...
                '/with/key/', makerKey), ...
                'value1', stateMsg, ...
                'value2', char(timeSinceChange, 'hh:mm'));

All of the MATLAB code can be deployed on ThingSpeak and scheduled to be executed periodically without having this on your desktop computer. The complete Smart Humidity Sensor project tutorial is available on Hackster.io. Feel free to discuss on the MATLAB Maker Community.

A storm undergoes bombogenesis when its central low pressure drops at least 24 millibars in 24 hours, according to the National Oceanic and Atmospheric Administration (NOAA).

At the MathWorks headquarters in Natick, MA we have a weather station sending data to ThingSpeak for the past several years. Here’s what the weather station looks like on a better day.

Not many interesting events emerge from the data, but with something called a bomb cyclone, Rob Purser decided to take a closer look using MATLAB. Our weather station on ThingSpeak channel 12397 collects temperature, humidity, and pressure data. By taking a look at this MATLAB plot of the pressure analyzed over 24 hours, you will see the pressure drops at least 24 millibars in 24 hours and in fact over 40 millibars. This storm definitely fits its name of explosive cyclogenesis.

Have a look at the raw data from ThingSpeak and see if you can determine the bomb cyclone event. In MATLAB, use thingSpeakRead via the ThingSpeak Support Toolbox. We documented the process of analyzing the weather station data using MATLAB on Hackster.io. Just follow the steps using MATLAB or MATLAB Online, to discover some interesting results.

Stay warm.

Naman uses an Arduino for processing the heartbeat data and turns the data into heartbeats per minute. Then, periodically, the device sends the data to ThingSpeak for data storage and data analysis using MATLAB. The heart rate monitor is connected to the internet using DFROBOT’s ESP8266 Wi-Fi Bee. The Wi-Fi Bee turns serial data-to-Wi-Fi.

This heart rate monitor sensor is a pulse sensor which is developed based on PPG techniques. This is a simple and low-cost optical technique that can be used to detect blood volume changing in the microvascular bed of tissues. It is relatively easy to detect the pulsatile component of the cardiac cycle according to this theory.

To build your own, check out Naman’s tutorial on either Hackaday or Hackster.

The digital twin is a federation of data and models that can be analyzed or put into a simulation to create useful information about the past, present, or future of the DT’s physical twin.

Bruce Sinclair of the Iot-Inc. Business Show podcast invite Jim Tung, a MathWorks fellow, to discuss models, simulation, and digital twins. Jim shares information about a few MathWorks customer use cases and our products used for modeling, simulation, and IoT.

Bruce and Jim talk about many interesting and key topics for IoT system development, including:

• The difference between data-driven models and functional models
• Using raw data to calibrate, update and validate functional models
• System level modeling
• The importance model hierarchy to discover insights
• A smart grid digital twin example
• The role of deep learning in digital twin modeling
• The actionable steps of how to create your digital twin

To listen to the  Iot-Inc. Business Show podcast, either subscribe on iTunes or play the episode on Iot-Inc.

Additional Resources

• MathWorks IoT
• MathWorks Predictive Maintenance
• MathWorks Data Analytics
• Iot-Inc. Article: The IoT Product Versus the Smart and Connected Product
• Iot-Inc.Podcast: Producing the Digital Twin for the Industrial Internet of Things

This year, I will be hosting an IoT workshop with Dr. Gerald W. Recktenwald, Portland State University, Jeff Branson from SparkFun, Dr. Duncan James Bremner P.E. from the University of Glasgow. Our session is called, “Your Head in the Clouds: A Hands-on Workshop on Using IoT Devices as Teaching Aids“. We will explore IoT hardware and software to be used as teaching aids in engineering education. We will use tools from SparkFun, ThingSpeak, and MATLAB to build an IoT project in the workshop. For more information, visit the ASEE session website.

American Society for Engineering Education Conference

Greater Columbus Convention Center
400 N. High Street
Columbus, OH

W426B·WEDNESDAY WORKSHOP:

Your Head in the Clouds: A Hands-on Workshop on Using IoT Devices as Teaching Aids
Wednesday,  June 28, 2017 1:30 PM to 5:30 PM

Statistics and Machine Learning Toolbox

Signal Processing Toolbox

When you are logged into ThingSpeak using your MathWorks Account, you can use functions from the following toolboxes if you are licensed to use them:

With multiple sensors around my house or office, I want to be able to send data to multiple ThingSpeak channels. But, when I want to perform data analysis, I have a hard time working with data from multiple channels. The channels do not have the same time stamps and are out-of-sync with each other.

With R2016b of MATLAB^®, I am able to use the new timetable data container. Once the data is a stored as a timetable, I can perform powerful operations such as retime, synchronize, and rmmissing.

In this example, I have two sensors outside of my office here in Natick, MA. One sensor is a temperature sensor that is sending data to ThingSpeak channel 163540. My other sensor is writing humidity data to channel 163545. Both channels are public. My goal is to plot temperature versus humidity over one time series. To accomplish this, I will use timetable and synchronize inside of a new MATLAB Visualization on ThingSpeak.

% Read from the temperature channel
temperatureTT = thingSpeakRead(163540,'Fields',1,'NumPoints',100,'outputFormat','timetable');

% Read from the humidity channel
humidityTT = thingSpeakRead(163545,'Fields',1,'NumPoints',100,'outputFormat','timetable');

% Synchronize two timestables and fill in missing data using linear interpolation
TT = synchronize(temperatureTT,humidityTT,'union','linear')

% Plot Temperature and Humidity over time
plotyy(TT.Timestamps,TT.Temperature,...
       TT.Timestamps,TT.Humidity);
        
title('Temperature and Humidity Synchronized From Two Channels')
xlabel('Temperature and Humidity in Natick, MA')
legend('Temperature','Humidity')

The first part of the script reads in ThingSpeak data from two different channels and stores the data in two timetables. Once the data is stored in a timetable, I am able to take advantage of synchronize. With synchronize, I can combine both timetables with one time series and fill in missing data using linear interpolation. This results in a plot that shows my data over time without any missing data. To create the plot, I signed into ThingSpeak, selected Apps, and created a new MATLAB Visualization with my MATLAB code.

All ThingSpeak users are able to try this example or explore the other new MATLAB features directly on ThingSpeak. I will leave my temperature (163540) and humidity (163545) channels public, so you can try out timetable example without having to connect devices to ThingSpeak.

Allie uses the MATLAB Analysis app on ThingSpeak to check her to do list and see if anything is overdue. If a task is overdue, the MATLAB code writes the task overdue into a ThingSpeak channel. The MATLAB code is very straightforward and does a bit of analysis on her task list to see what is overdue. To get the MATLAB Analysis code to keep checking her task last, she schedules the MATLAB code using the TimeControl app on ThingSpeak.

Allie also has other ideas on how to make use of her status light:

• Alert you when you’ve forgotten to water your plants
• Tell you when you’re out or range of important objects like your keys or wallet
• Combine with IFTTT to alert you when you’ve forgotten to respond to emails or phone notifications

To build your own Remembrall light, follow the step-by-step tutorial on Instructables.

MATLAB Central is “a place where you can get answers.” We have over 100,000 community members and MathWorks employees all sharing projects and files, experience, and answering questions. And, ThingSpeak is showing up on MATLAB Answers and File Exchange. This is great news for the ThingSpeak Community. If you already have a MathWorks user account and use it on ThingSpeak, you already have access to MATLAB Central. All you have to do is sign in. If you are new to MathWorks, you can sign up for a free user account to gain access to MATLAB Central and other features of ThingSpeak.

Check out Ned Gulley’s post, “Going Way Back with MATLAB Central” to learn about how the MATLAB community has formed over the years.

Cheers to MATLAB Central hitting the 15th year mark! We are happy to be a part of the story.

The Top 10 Internet of Things Countries*

1. United States
2. India
3. Germany
4. United Kingdom
5. Italy
6. Brazil
7. France
8. Poland
9. Canada
10. Spain

*According to ThingSpeak Usage Stats

The Squirrel Cafe is connected to the ThingSpeak IoT Analytics platform using the Raspberry Pi. The Raspberry Pi collects data from a tilt sensor, temperature sensor, and a camera to determine how many nuts the squirrels are taking. Whenever the lid opens, the current temperature gets measured by the DS18B20 sensor and sent to ThingSpeak for storage and analysis using MATLAB.

Carsten is also testing a theory. He noticed through observation that there might be a correlation between the number of nuts that get taken from the feeder and how long the coming winter season will be. This winter forecast and “nuts per minute” calculations are being performed by ThingSpeak’s MATLAB Analysis app. We are excited to see what the results prove in the next few years.

For full project details and source code, visit Carsten’s website for this project at www.TheSquirrelCafe.com.

The Internet of Things typically involves a discussion of smart devices and the cloud, with much less attention paid to the data collection, pre-processing of acquired data, and development of real-time analytics algorithms. A successful data analytics strategy involves embedded sensor analytics, historical data analysis, and online analytics. In this hands-on session, each participant will work with devices and try out the various types of analytics in action.

IoT Evolution West 2016

Caesars Palace, Las Vegas
900 Convention Center Blvd
New Orleans, LA

IOTD-02: Prototyping IoT Analytics: Hands on with ThingSpeak and MATLAB
Tuesday, July 12, 2016 at 2PM
Forum 15

The IoT panel at ASEE will be moderated by Dr. Gerald W. Recktenwald and features Dr. Jacob Segil from the University of Colorado, Boulder, Dr. Duncan James Bremner P.E. from the University of Glasgow, and Hans Scharler from MathWorks.

American Society for Engineering Education Conference

New Orleans Convention Center
900 Convention Center Blvd
New Orleans, LA

T426·IoT: What Do Students Need to Learn to Be Successful in this Field?
Tuesday,  June 28, 2016 1:15 PM to 2:45 PM

The project also shows you how to use MATLAB to get very detailed visualizations and data analysis of the data collected by the weather station. Some of the examples include histograms of temperature, humidity, and pressure, curve fitting, daily comparisons, and 3D plots of temperature.

Visit Hackster.io for the complete tutorial to build your own weather station, connect it to the internet with the Particle Photon, collect your data with ThingSpeak, and do data analysis with MATLAB.

[via Hackster.io]

The Arduino MKR1000 is a great starting point when learning about the “things” in IoT. The MKR1000 has a microcontroller, Wi-Fi module, encryption module, and a battery-charging circuit. It’s easy to get started and once you get it connected to ThingSpeak, you have a lot of “cloud power”. ThingSpeak has a suite of apps to allow the Arduino to post messages to Twitter, do data analysis, show charts and visualizations, and be controlled by schedules and external events. With these building blocks you can prototype any IoT system.

Once you have your data on ThingSpeak, you can analyze and visualize the data with built-in MATLAB apps.

[via ThingSpeak Tutorials]

Even if you do not have devices and sensors sending data to ThingSpeak, you can still use ThingView to see public channels. For example, if you want to see the charts created by sensors in my house, just add Channel ID 9 to ThingView. You see charts of light levels and temperature generated by my house.

Check out ThingView on Google Play!

The ThingSpeak platform joins the likes of Amazon Echo, ESP8266 Wi-Fi, and Particle.io platform. ThingSpeak users can easily document, share, and reproduce hardware and Internet of Things projects using Hackster.io. We are already off to a great start with 13 documented projects and tutorials and 31 community members. Check out our platform on Hackster.io to discover great projects and build your own IoT projects.

Loren explores the data using MATLAB Analysis and MATLAB Visualizations app built into ThingSpeak.

Offline Analysis: Analyzing Data stored on ThingSpeak

If you have desktop MATLAB, you can gain even more insights into our traffic data or any of your ThingSpeak Channels. You need to first import the data from ThingSpeak into desktop MATLAB. To simplify the retrieval of the data from ThingSpeak, we use the functions from the ThingSpeak Support Toolbox, available on MATLAB Central File Exchange.

readChannelID = 38629;
readAPIKey = '8NPXB8G515OAD94Q';

%% Read Data %%
[data, time] = thingSpeakRead(readChannelID,'DateRange',[datetime('Mar 13, 2016'),datetime('Mar 14, 2016')],'ReadKey', readAPIKey);

[via Loren on the Art of MATLAB]

The Electron is a new 3G connected IoT device using cellular data and works anywhere you can get 3G in the United States. It is really awesome to plug-in a device and get it connected without the issues of Wi-Fi. This development kit also makes it possible to build battery-powered, mobile sensors. Good thing that ThingSpeak supports GPS data and offers sensor data analytics.

Check out Julien’s tutorial to go “From 0 to IoT in 15 Minutes” and other ThingSpeak projects on Hackster.io.

Chris’ home solar water heating system is an example of an IoT application that uses multiple sensors to collect data about a physical system.  Chris’s water heater measures ambient temperature, stored water temperature, collector temperature, and pump speed. All of this data gets collect by ThingSpeak and stored in Channel 29633.

On days when the stored water temperature exceeds 50°C (122°F), there’s no need to use other methods to heat the store of water to a useful working temperature.  The pump should turn on only when the collector temperature is greater than the temperature of the stored water tank. If the pump turns itself on when the collector is cooler than the stored water temperature, valuable heat is lost from the stored water tank. Chris wants to be alerted of this condition, so that he can adjust the controller settings and increase the efficiency of the system.

IoT systems like Chris’ solar water heating system, typically gather large amounts of data but often the real interest is in events that occur infrequently. The ability to take action when these infrequent events occur is important and requires a mechanism to detect such an event and launch an action. We are going to use the data collected by the solar water heating system stored in the ThingSpeak Channel 29633 and use the MATLAB Analysis app to detect a condition and alert him using Twitter.

MATLAB Event Detection

To detect an erroneous pump behavior event, create a new MATLAB Analysis on ThingSpeak with the following code:

% Read data from fields 1, 2, and 3 from channel 26633.
% Field 1 represents the stored water temperature
% Field 2 represents the collector temperature
% Field 3 represents the state of the pump - on or off
[data, time] = thingSpeakRead(29633, 'Fields', [1, 2, 3]);

% Assign measurements to individual variables
storeTemp = data(1);
collectorTemp = data(2);
pumpState = data(3);

% Check if collectorTemp is less than storeTemp
isCollectorCooler = collectorTemp < storeTemp;

% Identify if pump is on while the collector is cooler.
% We apply a logical AND operation to detect an event only when collector
% is cooler than store temperature and the pump is on.
eventDetected = isCollectorCooler & pumpState

Press the ‘Run & Save’ button to save the MATLAB Analysis App. The code above sets eventDetected to 1 every time the collector temperature is cooler than stored temperature and if the pump is on. Now that we can detect the event, we need to set the MATLAB App to be run on a schedule. To do this, we will setup a TimeControl to run our MATLAB code every 5 minutes.

Sending Alerts using MATLAB Analysis

So far, we’ve created a MATLAB Analysis to detect events in the data being collected in the solar water heater data channel. We associated our MATLAB Analysis code with a TimeControl to have it run every 5 mins to check for our event of interest. To receive a notification via Twitter when the pump is on incorrectly, we can use MATLAB Analysis to send a Tweet.

First, you need to link your Twitter account to your ThingSpeak account. Then, add the following lines of code at the end of your MATLAB Analysis code to send a Tweet when an event is detected:

If eventDetected
webwrite('http://api.thingspeak.com/apps/thingtweet/1/statuses/update',
'api_key', '<ThingTweet_APIKey>', 'status', 'Alert! Solar pump error!')
end

Be sure to replace <ThingTweet_APIKey> with your ThingTweet API Key.

If the solar water heater pump turns on at the wrong times, you will get a Tweet to let you know!

Next Steps

This example shows you the power of some of the ThingSpeak apps that we make available to you to experiment with. The MATLAB Analysis app is really powerful and can be used to detect events in your data and send alerts. MATLAB Analysis can be used for all sorts of calculations and orchestrations of different web services. We could have also used MATLAB to control the pump.

Feel free to try this example and take it further…

• Reading data from fields in different channels
• Combining data from fields in a channel and data read from a website such as a weather station or weather forecast.

What will you MATLAB?

By following our tutorial, you will be able to use ThingSpeak to send messages to your team’s Slack channel. This will also allow devices like an Arduino to use Slack since ThingSpeak will take care of authentication and HTTPS.

You only need a few things to build a light and temperature sensor that writes data to ThingSpeak:

• Arduino Zero or Uno Board
• Arduino Wifi Sheild 101
• Photocell
• Temperature Sensor (This example uses a TMP36)
• 10K Ohm Resistor

Once you have the circuit built, you create a ThingSpeak channel, connect the Arduino WiFi 1010 to your Wi-Fi network, and install the source code from the tutorial on the Arduino.

Data is now being sent to your ThingSpeak Channel. Go to your channel to see two charts of the light and temperature data. To take the project a step further, go to ThingSpeak Apps and use MATLAB to analyze and visualize and trigger actions from the data.

[via Arduino.cc]

This webinar will show how you can use MATLAB and Simulink with ThingSpeak, an Internet of Things data collection platform. ThingSpeak can be used to collect, analyze and act on data sent from devices such as Raspberry Pis and Arduinos. To illustrate this, a car counter is implemented overlooking a busy highway using a Raspberry Pi 2 and a webcam.  In this demonstration, Simulink is used to deploy the car-counting algorithm on the Raspberry Pi which is connected to ThingSpeak. The traffic can be analyzed offline with MATLAB or online using ThingSpeak and its built-in MATLAB Analysis and MATLAB Visualizations apps.

Eric Wetjen has been working in Product Marketing at MathWorks for the last 7 years. He focuses on bringing MATLAB analysis capabilities to low cost hardware, Test and Measurement equipment and Internet of Things devices.  Prior to MathWorks, Eric held various positions in Product Management and Application Engineering primarily in the telecom industry.  Eric holds a Ph.D. in Engineering from Brown University.

Sign up at element14. 

We write the temperature and humidity values from the weather station to a ThingSpeak channel. At some point in the project, we started to wonder about dew point calculations. We wrote some MATLAB code that combined the temperature and humidity to calculate dew point. I did this using the ThingSpeak app, “MATLAB Analysis”. You can try this out with ThingSpeak now by signing in, selecting Apps, MATLAB Analysis, New, selecting “Calculate Dew point”, and clicking “Create”. This happens to be one of our built-in examples using our weather station’s public data.

It is great that it was easy to calculate dew point with MATLAB, but I want to see this analyzed data over time just like any other sensor data. The solution is a powerful combination of MATLAB Analysis and TimeControl. We use MATLAB Analysis to do the analysis and write the data to a ThingSpeak channel. Then, we use the TimeControl app to repeat the analysis every 5 minutes.

To setup MATLAB Analysis on a schedule, sign into ThingSpeak, select Apps, TimeControl, and New TimeControl.

My MATLAB code now runs every 5 minutes doing analysis and writing data to my ThingSpeak channel. The TimeControl settings can be tailored to your needs such as executing MATLAB code once a day or only on weekends. This combination of MATLAB Analysis + TimeControl allows you to create continuous analysis of your project data.

To try this out for yourself, we have a public channel of weather station data that we have collected in Natick, MA at our headquarters. You can use that data and do your own MATLAB Analysis and writing the results back to your own channel. Also, Check out the ThingSpeak Documentation where we have a complete tutorial for you to help get started with ThingSpeak and MATLAB.

Arduino IDE Installation

In the Arduino IDE, choose Sketch/Include Library/Manage Libraries. Click the ThingSpeak Library from the list, and click the Install button.

Particle / Spark IDE Installation

In the Particle/ Spark Web IDE, click the libraries tab, find ThingSpeak, and choose “Include in App”.

Compatible Hardware

• Arduino or compatible using an Ethernet or Wi-Fi shield (we have tested with Uno and Mega)
• Arduino Yun running OpenWRT-Yun Release 1.5.3 (November 13th, 2014) or later.
• Particle Core or Photon (Formally Spark)

ThingSpeak Examples

The library includes several examples to help you get started.

• CheerLights: Reads the latest CheerLights color on ThingSpeak, and sets an RGB LED.
• ReadLastTemperature: Reads the latest temperature from the public MathWorks weather station in Natick, MA on ThingSpeak.
• ReadPrivateChannel: Reads the latest voltage value from a private channel on ThingSpeak.
• ReadWeatherStation: Reads the latest weather data from the public MathWorks weather station in Natick, MA on ThingSpeak.
• WriteMultipleVoltages: Reads analog voltages from pins 0-7 and writes them to the 8 fields of a channel on ThingSpeak.
• WriteVoltage: Reads an analog voltage from pin 0, converts to a voltage, and writes it to a channel on ThingSpeak.

Complete open source code and examples for the ThingSpeak Library are available on GitHub. Discover other MathWorks Open Source and Community Projects on GitHub.

ThingSpeak is an open data platform for the Internet of Things. Your device or application can communicate with ThingSpeak using a RESTful API, and you can either keep your data private, or make it public. In addition, use ThingSpeak to analyze and act on your data. ThingSpeak provides an online text editor to perform data analysis and visualization using MATLAB^®. You can also perform actions such as running regularly scheduled MATLAB code or sending a tweet when your data passes a defined threshold. ThingSpeak is used for diverse applications ranging from weather data collection and analysis, to synchronizing the color of lights across the world.

At the heart of ThingSpeak is a time-series database. ThingSpeak provides users with free time-series data storage in channels. Each channel can include up to eight data fields. This tutorial provides an introduction to some of the applications of ThingSpeak, a conceptual overview of how ThingSpeak stores time-series data, and how MATLAB analysis is incorporated in ThingSpeak.

[via MathWorks]

The project uses a Raspberry Pi 2 and USB webcam acting as a sensor. The webcam picks up traffic flowing in both directions. Once the algorithm for detecting cars is modeled in Simulink, the algorithm gets deployed on the Raspberry Pi. The Raspberry Pi sends the raw data to ThingSpeak on regular basis where it is analyzed using the MATLAB Analysis app on ThingSpeak.

After sending to ThingSpeak, Eric created a MATLAB Analysis app to calculate the daily traffic-volume on ThingSpeak Channel 51671. Now that the data is public, others could use this processed data within apps such as Waze to optimize directions using analyzed traffic flows.

Check out the article for the complete project details and all of the code to get your Raspberry Pi + ThingSpeak analysis project started.

On ThingSpeak, so far, the datetime function returned time set to UTC time zone by default. Starting at 10 am (EDT) on September 10^th 2015, the datetime function will return date and time set to your account time zone (at https://thingspeak.com/account). This will allow you to read data from your channel with timestamps zoned to your local time zone instead of UTC.

For example, my account time zone is set to Eastern Time (US & Canada), and when I ran the following MATLAB code at 12:23 pm, I received:

dt = datetime('now')
dt =
     10-Sep-2015 12:23:35

Prior to this change, I would have received:

dt =
     10-Sep-2015 16:23:35

As you can see, the timestamp is 4 hours ahead of my time zone, which was due to MATLAB returning time in UTC.

This change makes it easier for you to perform time related activities in your time zone. Note that this new feature is available for both thingSpeakRead and thingSpeakWrite functions as well. As an example, consider the following request to read data from the MathWorks Weather Channel:

MATLAB Code:

[data, timestamp] = thingSpeakRead(12397);
display(timestamp.TimeZone, 'TimeZone');

Output:

data =
     225.0000    3.8000   43.9000   95.8000
     0   29.9800    4.3000    0.0300
timestamp =
     10-Sep-2015 16:13:54
TimeZone =
     America/New_York

With this enhancement, you would no longer have to explicitly specify the time zone of your dates and time to read and write data in your time zone.

Here are a few other examples:

1. Read data corresponding to one entire day in your timezone:

startDateTime = datetime('September 10, 2015 00:00:00')
endDateTime = datetime('September 10, 2015 23:59:59')
readChannelID = 12397;
[data, timeStamps] = thingSpeakRead(readChannelID, 'DateRange', [startDateTime, endDateTime])

2. Read data between certain hours of a day (between 7 am and 9 pm):

startDateTime = datetime('September 10, 2015 07:00:00')
endDateTime = datetime('September 10, 2015 21:00:00')
readChannelID = 12397;
[data, timeStamps] = thingSpeakRead(readChannelID, 'DateRange', [startDateTime, endDateTime])

3. Generate a MATLAB plot in your local time zone:

[data, timeStamps] = thingSpeakRead(12397, 'Fields', 3, 'NumPoints', 10);
plot(timeStamps, data)

Note that, if at present, you are explicitly setting the time zone to your local time zone, you might see unexpected behavior in your code. Here are a few examples, based on support requests we have received:

1. If you are using datetime function in your code similar to the example below:

% Set the time now to variable dt
dt = datetime('now')
% Assign time zone to UTC since the dt is unzoned by default
dt.TimeZone = 'UTC';
% Convert the timestamp to ‘America/New_York’
dt.TimeZone = 'America/New_York'

To fix this, remove the “TimeZone” assignments since time is now returned in your time zone by default, and use the code below:

% Set the time now to variable dt
dt = datetime('now')

2. If you are setting the time zone of data returned by thingSpeakRead to your zone:

% Read data from a channel
[data, timeStamps] = thingSpeakRead(12397);
% Set the timezone to match your zone
timeStamps.TimeZone = 'America/New_York';

To fix this, remove the line with the “TimeZone” assignment, and use the code below:

% Read data from a channel
[data, timeStamps] = thingSpeakRead(12397);

For more information about the datetime function refer to the MATLAB documentation. If you need support, use the MATLAB section of the ThingSpeak Forum.

CheerLights uses ThingSpeak to collect the latest color. We get the color value by following “CheerLights” on Twitter using the TweetControl app. When someone Tweets using “CheerLights” and a color name, the TweetControl app writes the color to the CheerLights Channel on ThingSpeak. Other developers wanting to join the CheerLights project read in the latest color value using the ThingSpeak Channel API and then set their light color to the same one.

With some MATLAB Analysis and Visualizations, I know that currently red is the most popular color on CheerLights! I have recently taken advantage of the MATLAB integration with ThingSpeak. Under Apps -> MATLAB Analysis, we have an example that will show you how to analyze the public CheerLights Channel on ThingSpeak to determine the most requested color. The MATLAB Analysis example is called, “Analyze text for the most common color”.

Example MATLAB Visualization Code

lights = thingSpeakRead(1417,'OutputFormat','table','NumDays',30);
hist(categorical(lights.LastCheerLightsCommand))
set(gca,'XTickLabelRotation',45)

People all over the world have joined CheerLights by making all kinds of light displays, apps, and browser plugins. I recently created a CheerLights display for my parents using a LIFX Wi-Fi Light Bulb. If you want to control all of the lights, just send a Tweet using Twitter that mentions @CheerLights and a color.

“@CheerLights Let’s go Blue!”

Check out CheerLights.com for more detail and for ideas on how to join the project. We are all connected!

We have been working with the MATLAB team at MathWorks to provide two new ThingSpeak Apps: MATLAB Analysis and MATLAB Visualizations. With these new built-in Apps, the ThingSpeak web service can automatically run MATLAB code. That makes it easier to gain insight into your data.

With the MATLAB Analysis app, I am now able to turn my home’s temperature and humidity data into dew point. Dew point is important to find out if the environment is comfortable independent of just knowing the temperature alone. If the dew point is too high or too low, your guests may notice their glasses sweating or that they are uncomfortable.

I am also able to clean up my sensor data and filter out bad data and write it back to a new ThingSpeak channel. From time to time, I see one of my sensors report a really high value, and I’d like to have a way to fix it.

We have provided many MATLAB code examples to get started quickly.

Some of our analysis examples include:

• Calculate Average Humidity
• Calculate Dew point
• Convert Celsius to Fahrenheit
• Eliminate data outliers
• Convert Fahrenheit to Celsius
• Calculate hourly max temperature
• Replace missing values in data

With MATLAB Visualizations, we made it way easier to chart data from multiple data fields. By selecting the “Wind Velocity” example MATLAB Visualization, I can see a plot of the wind velocity data collected by my weather station.

Other visualization examples include:

• View temperature variation over the last 24 hours using a histogram
• Plot wind velocity over the last hour using a compass plot
• Understand relative temperature variation
• Plot data from multiple fields
• View temperature and pressure levels
• Visualize relationship between temperature and humidity

Are you looking for an easy way to connect your Arduino or Raspberry Pi devices to ThingSpeak? We have also been working with the MATLAB team at MathWorks on some Hardware Support Packages to help with that. I’ll talk about that in a future blog!

This is really big news for the ThingSpeak Community. I am really excited to see what you do with these new apps. I will share projects on the blog as they come in. Let’s find out together what all of this data means. Get started at ThingSpeak.com!

The nodeIT is centered around the ESP8266 Wi-Fi microcontroller and allows you stack other boards to extend its base functionality. Once the nodeIT is connected to your Wi-Fi network, you can easily publish data to ThingSpeak and visualize the results, such as data collected by a barometric sensor.

For more information about nodeIT, follow their Kickstarter campaign and check out their ThingSpeak Room Monitor project.

[via Kickstarter]

The sensor records the PM10 and PM2.5 dust levels to get an accurate indication of the dust in the air. This project is a great example of how a little sensor could turn into something important for protecting machine shops, construction sites, and garages.

[via shadowandy / GitHub]

Hackaday.com wrote an article about Vegard’s soldering iron connected to the Internet of Things. Here’s what they had to say:

The data pushes out to the ThingSpeak server which handles pushing data out to the bigger network, and data representation (like the cool Google gauge…). The best part: [Vegard] gets a phone notification when he accidentally leaves his soldering iron on. How perfect is that?

That looks a lot like our desks… wires, microcontrollers, pliers, cutters, Wi-Fi modules, and soldering irons. And now, the soldering iron is on the Internet of Things.

[via Vegard Paulsen / Hackaday.com]

[via Twitter]

RunnerHam Tweets his distance and time when he takes a run on his wheel, “I’m done! 57.62m at 0.61m/s”. You can also check out his ThingSpeak Channel where he records lots of data about his day.

Ángel also released an Instructables explaining his “pet project” so you can make your own and make your own enhancements. Just imagine what you can do with some sensors, connectivity, and ThingSpeak Web Services!

[via Instructables]

[via GitHub]

Greg built the project out of necessity to help his plants suffering from “localized drought”. Let’s hope his plants get proper watering and that other ThingSpeak users can quickly and easily build this project. Thanks for sharing!

[via Instructables]

>> ThingSpeak ESP8266 Forum <<

“We switched to SSD drives for all of our database servers,” said Lee Lawlor, Lead Engineer of ThingSpeak. “All of the upgrades are live and available to the entire ThingSpeak Community!”

The improvements decreased response time dramatically and improved large data set retrieval by ten times.

The idea is that it is frustrating waiting for luggage at the airport and wondering where it is and why it is not on the baggage carousel. With this project, you can track luggage from start to finish. The advantages are not only for the traveler, the airlines could track luggage as well and get quality statistics for each airport. And, the base system has many applications outside of travel such as the Automotive Industry.

Chris explains the project really well on his blog and with a YouTube video.

For more information check out the FastLED and Arduino tutorial and the Arduino Sketch on GitHub.

As more and more users create TweetControls, the service started slowing down. We have enhanced how the service works and now you get instant TweetControls!

In an Instragram video sending a Tweet and changing the CheerLights color, you will see that there is little delay between sending the Tweet and executing the control command to change the colors on his Christmas tree.

Learn more about TweetControl on ThingSpeak Docs.

The first step is to link a Twitter account to ThingSpeak.

Next, we’ll create a new TimeControl with the following values:

• Name: “CheerLights to Twitter”
• Frequency: “Recurring”
• Days: “Mon”, “Tue”, “Wed”, “Thu”, “Fri”
• Time: “9:40 am”
• Fuzzy Time: “+/- 10 minutes”
• Action: “ThingTweet”
• Tweet: “It is currently %%datetime%% and CheerLights is %%channel_1417_field_1%%.”
• Twitter Account: (select your linked Twitter account)

Save this TimeControl and you’re finished.  Every weekday within 10 minutes of 9:40 am in your timezone, TimeControl will send a Tweet with the current datetime and the current CheerLights color.

The CheerLights Channel ID is 1417, and colors are saved in field 1, so %%channel_1417_field_1%% will be replaced with the current CheerLights color.  You can change these values to access the most recent data from your own Channels.

Here’s an example Tweet from this tutorial.

Everything you need to know in order to build your own sensor logging project is on Noel’s Instructables.

[via Instructables]

Introducing TimeControl…

TimeControl is a web service hosted by ThingSpeak that executes any type of HTTP service call or sends Tweets at predetermined times or schedules. We now offer one-time commands and weekly recurring schedules for commands. TimeControl executes a ThingHTTP or ThingTweet command, and ThingHTTP can interface with any external Web Service API by doing SSL, Basic Auth, custom HTTP headers, GETs, POSTs, PUTs, and DELETEs. ThingHTTP simplifies connecting low-power, low-resource microcontrollers to complex web service APIs such as Twilio, Xively, and Amazon. When you combine TimeControl + ThingHTTP, you get scheduled triggers to any web service you can imagine and scheduled control of an embedded IoT device.

Example TimeControl Projects

• Turn on your lights every day at 4pm
• Set a feeder schedule on your aquarium
• Schedule full control of ioBridge and RealTime.io devices
• Send a Tweet of your Channel’s last value every morning

ThingSpeak IoT Apps

• ThingTweet – Link your Twitter account to ThingSpeak and send Twitter messages using our simple API
• ThingHTTP – Create custom POSTs or GETs to other web services and retrieve the data
• TweetControl – Listen to commands from Twitter and then perform an action
• React – Perform actions when conditions are met by your data in your channels
• TalkBack – Allow devices to execute queued commands
• TimeControl – Automatically execute ThingHTTP requests at predetermined times and schedules 

TimeControl Resources

• TimeControl Documentation
• ThingHTTP Documentation
• Create a new TimeControl 

We have come a long way since building cloud platforms for connected devices – now known as “The Internet of Things”. Here’s what our first website for the ThingSpeak project looked like 5 years ago…

Get started with IoT now:
Visit ThingSpeak.com or fork the project on GitHub!

POST https://api.thingspeak.com/update
     api_key=XXXXXXXXXXXXXXXX
     field1=73
     metadata={"officeTemp":73}

The full ThingSpeak Channels API is available on ThingSpeak Docs.

[via ThingSpeak Forums]

First, create a new Channel, and fill out the field names as follows: Field 1 = “CPU Usage (%)”, Field2 = “Memory Usage (%)”, Field 3 = “Disk Usage (%)”.

Next, add the open-source server statistics script to your server, which can be found at: https://raw.githubusercontent.com/iobridge/thingspeak/master/lib/server_stats.sh

Inside the script there’s an API Key variable, which should be replaced with your specific Channel’s API Key (leave the single quotes, and only replace the X’s): api_key='XXXXXXXXXXXXXXXX'

For the script to work properly, install the “bc” package via: sudo apt-get install bc

Then make the script executable: chmod +x server_stats.sh

Finally, edit your crontab file: crontab -e

Make the script execute every minute by adding this line to your crontab (make sure you use the proper path to the script): * * * * * /path/to/server_stats.sh

The script will then automatically POST server stats to the Channel specified by the API Key every minute.

You can see some of the ThingSpeak server statistics here:

• https://thingspeak.com/channels/12370
• https://thingspeak.com/channels/12371

About Phusion Passenger

Phusion Passenger is a web server and application server for Ruby, Python, Node.js and Meteor web apps. It makes web app deployments a lot simpler and less complex, by managing your apps’ processes and resources for you.

What makes it so fast and reliable is its C++ core, its zero-copy architecture, its watchdog system and its hybrid evented, multi-threaded and multi-process design.

Here’s what the new Spline Chart looks like:

Here’s a regular line chart:

And just as a reminder, here are all of the supported ThingSpeak Chart types:

• type=bar
• type=column
• type=line
• type=spline

For complete ThingSpeak Charts documentation, check out ThingSpeak Docs.

We keep hearing about how many Billions and Billions of things there will be connected. Just think about how much data that they will create! Yep, it’s Big Data, or even, Bigger Data. ThingSpeak is the only open data platform specifically designed for the Internet of Things available ‘in the cloud’ or on your own network to capture and distribute data from things.

A new homepage for ThingSpeak

When we look out into the Cosmos, we see Billions and Billions of stars and keep a fond memory of Carl Sagan in our hearts. As we connect this planet, we can’t but think of the scale and the magnitude that IoT will bring. Using this inspiration, we launched the new ThingSpeak.com!

Carl Sagan said, “We have lingered long enough on the shores of the cosmic ocean, we are ready at last to set sail for the stars.” We believe the same about the Internet of Things! Let’s get going!

Electric Imp is a connectivity platform for connecting Wi-Fi devices to cloud services, much like RealTime.io and Iota Wi-Fi modules and Spark.io. Some Electric Imp module’s come in an SD card form factor and adds Wi-Fi connectivity to what’s connected to the Electric Imp module. Access to the Electric Module happens via the Electric Imp cloud. While connectivity is simplified with the Electric Imp system, you will need a data service like ThingSpeak to complete the Internet of Things experience. Once data from Electric Imp devices are in ThingSpeak, you can easily build applications and interactivity with other devices and platforms.

We put together a quick start tutorial for the Electric Imp and ThingSpeak, so you can quickly and easily get the Electric Imp talking to ThingSpeak. The tutorial uses parts from SparkFun – the Electric Imp Wi-Fi SD module, breakout board, and USB cable / power supply.

Get started now…  Check out the official Electric Imp and ThingSpeak Tutorial and source code on GitHub.

All of the source code to connect Electric Imp to ThingSpeak and the 3D printer design files are available on Marcus’ blog ‘slickstreamer‘.

[slickstreamer / Dangerous Prototypes]

Mi Humidor de Cigarros conectado a Internet por medio de un Arduino

The Top 10 Internet of Things Countries*

1. United States
2. France
3. Poland
4. United Kingdom
5. Germany
6. Netherlands
7. Australia
8. Canada
9. Italy
10. Brazil 

*According to ThingSpeak Usage Stats

We have developed a new ThingSpeak App and it is available now to all ThingSpeak Users.

The new TalkBack App allows devices to check ThingSpeak for commands to execute. TalkBack is perfect for battery-powered devices that need to sleep most of the time and wake up to see if there is anything to do and then go back to sleep, like a door lock for example. The lock is mostly going to be asleep to save battery power, but it can wake up periodically and check TalkBack or be woken up by a button press to see if it should be opened or not.

Devices powered by ThingSpeak and now with TalkBack will be able to both push sensor data to the ThingSpeak Cloud and check TalkBack if any commands are available all in one request. To get started, we have the complete TalkBack API Documentation and an Arduino Yún Tutorial available now.

Atmel-powered Arduino Yún Tutorial

With the release of TalkBack, we created a tutorial for the Arduino Yún. The “Yún” is a special combination of easy-to-program Arduino with an additional processor, an Atheros AR9331, running Linux and the OpenWrt wireless stack. Programming the Arduino via USB is identical to the Arduino Leonardo. Once the Arduino Yún is connected to Wi-Fi, the Arduino has full access to ThingSpeak Cloud Services and the TalkBack App and API. Check out the Controlling the Arduino Yún with TalkBack tutorial for a step-by-step way of controlling the Arduino Yún via TalkBack and the ThingSpeak Cloud.

TalkBack is available now to all ThingSpeak Users and to new users by Sign Up for Free at ThingSpeak.com! Please feel free to share with us and the ThingSpeak Community with the awesome ways you use TalkBack with your ThingSpeak Projects!

Question: How do I find ‘public’ ThingSpeak Channels?

In order to help developers find open data inside of ThingSpeak Channels, we created a new API for searching the public ThingSpeak Channels.

Here are the Public ThingSpeak Channels. We order the channels by activity and completeness. Channels may be tagged and this helps find data that you might find interesting for your application. We also have API commands that you can pass to the ThingSpeak Channel API to return the public ThingSpeak Channels in either JSON or XML format.

Here are some easy examples:

• http://api.thingspeak.com/channels/public.json – Returns the Public ThingSpeak Channels in JSON format
• http://api.thingspeak.com/channels/public.xml – – Returns the Public ThingSpeak Channels in XML format
• http://api.thingspeak.com/channels/public.json?tag=temperature – Returns the Public ThingSpeak Channels in JSON format that match the tag, “temperature”

For support and questions, please use the ThingSpeak Forum.

Looking over the code for the Electric Imp, it looks pretty easy to cross-clouds from the Imp to ThingSpeak. Check out the source code on GitHub and full details on Slickstreamer.

[via Slickstreamer]

The OHS is an amazing experience. You get to meet all the Open Source Hardware heroes that are pushing this movement forward. This year there are many talks and panels covering all aspects of the open source hardware movement. Our part in all this is to push open platforms to connect all that open hardware. ThingSpeak is growing very quickly supporting the open hardware and software for advancement of the Open Source Internet of Things.

Sponsorship opportunities are still available!

For more information and Python source code, visit MY NERD JOURNAL.

[via MY NERD JOURNAL]

Drum roll… We are announcing, “The Easiest Contest Ever… Part 2”.

All you have to do is build a project using a ThingSpeak web service, post a demo / how-you-done-it video on YouTube or Vimeo, and tell us about it. We are giving away 20 gift certificates to SparkFun valued at $50 each. And… selecting our favorite projects for bonus prizes. Leave a comment with questions. This contest is open to anyone, so Sign Up for ThingSpeak and get going!

Disclaimer: All entries will be published on the ThingSpeak Community Blog and selection is based on meeting the described criteria. All rulings are at the final discretion of the ThingSpeak team members. Let’s see how crazy this will get!

Project Inspiration

Check out the public ThingSpeak Channels for what others have done already. Others have made Social Gumball Machines and Real-time Gas Sensors. Incorporate one of the many new features into your project such as REACT and TweetControl. Use a USB data logger with ThingSpeak Importer, track a car using ThingSpeak geolocation services, create a mashup using ThingSpeak Plugins…wait…we have said too much. We want to be surprised by what you come up with, so feel free to get creative.

New Features

It may have been some time since you have checked out ThingSpeak, so we wanted to share some of the new things other the past year.

Private and Public Views

Every ThingSpeak Channel now has a Private and Public view. A private view of your ThingSpeak Channel is only viewable by you. If you choose to make your ThingSpeak Channel public, you also have a public view of your channel info. Separating the views increases privacy and provides flexible usage of a ThingSpeak Channel. Each view is customizable so you can have a more detailed private view for and a customized view for public users.

Chart Builder

Chart Builder is now part of the channel views. You can customize your charts directly on your channel. An embed code for the chart gets generated automatically so you can integrate a chart on your own website quickly.

Custom Channels

Every thing about a ThingSpeak Channel is customizable. You can control which fields get displayed, add a map, add a YouTube video, add status messages, or add ThingSpeak Plugins. The elements on a ThingSpeak Channel can be moved around using a drag-and-drop user interface.

Plugins

When we first launched ThingSpeak Plugins we made the plugins only privately accessible. Now, you can make a ThingSpeak Plugin public and add it to your channel views. Plugins are great for making new features for ThingSpeak, such as multiple trend-lines on a chart.

Public Channel List

We often get requests to share a list of the public channels offered on ThingSpeak. We have developed a system to display active ThingSpeak channels and score each channel. A channel gets a higher score by being actively updated, adding tags, adding a description, and adding a demo video. Check out the public channels on ThingSpeak.

ThingSpeak React

React is our biggest undertaking yet and we spent a year developing a robust and scalable system for monitoring channel data in real-time. With the React app you can set triggers on your channels to cause other events to fire. For example, if your temperature gets too high, you can send a ThingHTTP request to a control system to cause an alert. Since ThingSpeak has location data baked in, you can also create geolocation reacts. If you get within 100 meters of a lat/long, you can trigger an action. This is perfect for building location-based thermostats.

Next…

We are working on many features that we will release over the summer. We are also going to update the Open Source version of ThingSpeak on GitHub too. Please stay tuned for some exciting news.

Indoor Environmental Quality (IEQ) is the measure of comfort and includes factors such as temperature, humidity, pressure, noise level, and indoor air quality. don’s original goal was to monitor the IEQ of his baby’s room, but he soon realized that his project has more applications around the house and for others.

Don published the full details of the project and submitted it to the Green Design Contest at Instructables. Great work!

[via Instructables]

The Supermechanical team put this combination of Twine and ThingSpeak to use right away. They created a “Productivity Quantification” system to capture events around the office and try to determine how productive they are. They were able to track how much coffee they were drinking, snacks they were eating, toilets they were flushing, and things they were finishing. The results are a quantified picture of office productivity and a beautiful display of the data via the ThingSpeak API.

Creating a Twine Action to push data to ThingSpeak is really easy to do. Here’s what it looks like…

To do more with ThingSpeak and Twine make sure to check out the tutorial on the Supermechnical blog.

[via Twine / SUPERMECHANICAL.BLOG]

We want to send a special thanks to powermik, oiotoshi, sekjal, and akinsgre for contributing new code and reporting bugs.

All of the latest code is available on GitHub, so start building your own Internet of Things today!

Duke says,

“A demo of how to use ThingSpeak (an IOT web site) with a Gadgeteer Gas Sensor Device. Data from the sensors are displayed in real time on ThingSpeak and using some of ThingSpeak’s cool features the Gas Sensor device can send out Tweets for Alert and Alarm conditions.”

Another awesome part of this project is that it uses .NET Micro Framework library, μPLibrary 1.8, created by [paolopat]. This library makes it really easy to tap into ThingSpeak web services by embedded devices. It’s great to see different parts of the project coming together from multiple ThingSpeak users. We appreciate the creative combinations and the efforts that you are putting into your projects. Thanks!

For more information, check out the live sensor readings on the project’s ThingSpeak Channel and download the complete source code at Codeshare.

[via >TinyCLR Forums]

One that has piqued our interest is the EVE Alpha for the Raspberry Pi created by Ciseco from Nottingham, United Kingdom. Wireless is a key part of the Internet of Things as with wireless we can connect more things in a more seamless way, then bridge them to the Internet. EVE Alpha aims at giving you a lot of wireless options in a tiny form factor all connected to an integrated computer called the Raspberry Pi.

Members of the ThingSpeak team are backers of this project and many others. We love finding new ways to get data to and from web services. This is exactly what we are here to do! We are looking forward to connecting the EVE to a host of web services (and ones we haven’t even released yet). Another key feature is the suite of wireless technologies that we want to prototype with all on one board. At the timing of this writing the EVE Alpha Kickstarter campaign is close to being funded, so there are high chances that Ciseco will deliver the Swiss Army knife of wireless development platforms!

[via Kickstarter]

.NET Micro Framework Developer [paolopat] created a client for the ThingSpeak platform. This allows any device that supports the .NET Micro Framework to access ThingSpeak web services by using the μPLibrary 1.8. The library is available on NuGet Gallery and abstracts the ThingSpeak API. The library works with the popular Netduino Plus and other devices running .NET Micro Framework.

Paolo says,

“With more and more embedded devices “smart” in the world, begins to take on an increasingly important concept of the Internet of Things (IoT), a neologism by which you want to express the capacity that these devices (brutally “things”) in order to connect to the world wide web and exchange information. In this come into play a number of online platforms that provide the service to upload and logging information in real-time making it available to other devices that request them. The architecture is oriented such that the platform is obviously RESTful where the data grouped into channels and feeds are accessible through the concept of URL.

One of the main platforms is certainly ThingSpeak, for which I have implemented a client for. NET Micro Framework and I have included in my library uPLibrary (now at version 1.8.0.0) present on CodePlex, namespace uPLibrary.IoT.ThingSpeak.”

Thank you, Paolo!

[via Embedded101]

[via Facebook]

UPDATE

Over at the Netduino forums, we found the source code for the Netduino and HTML for the ThingSpeak gauges for embedding the solar panel data on a website. Awesome!

This video that we discovered on YouTube is the team’s presentation. You will get to see ThingSpeak in action, live in front of an audience about halfway thru…

We hope you got an “A” on the project (do they still give letter grades?)!

Download the free Application Note, “Interfacing Flyport to ThingSpeak”, and the Source Code to get your Flyport connected to web services via ThingSpeak.

Since the project release, there has been much activity. A part from CheerLights being discussed on blogs like MAKE and Lifehacker, the community has created some interesting bits of tech that extend the project further than lights. So if you don’t have a way to connect your lights together with CheerLights, you can connect your mobile phone, browser, and web sites together by subscribing to the CheerLights feed. Right now you can check the latest CheerLights color with an Android App created by @ChrisLeitner. Another really neat thing is a browser plugin for Chrome designed by Josh Crumley. So, in the top corner of your web browser you can see the latest color in an unassuming way. It’s a little reminder that we are connected.

To join CheerLights, all you have to do is build something that subscribed to the CheerLights ThingSpeak Channel or access the data using JSON and XML. You can also use the apps, browser plugins, or web widgets to see the colors. Visit the CheerLights website hosted on Tumblr for details on making a controller with Arduino, ioBridge, or Digi’s ConnectPort.

To control CheerLights, just send a Tweet to @CheerLights and mention a color.

Just think when you send this Tweet that you are updating 1000’s of lights, apps, browsers, and widgets all at the same time.

Spread some cheer…

[via MAKE / Lifehacker / CBC / ioBridge Projects]

Topics on the agenda:

• Switch over to Ruby on Rails 3.1
• ThingSpeak v2.0
• Active ThingSpeak Projects
• Adding modularity and tests to the GitHub repository
• …btw, we’re hiring!

Background on ThingSpeak:

ThingSpeak is an open source web application and API to manage devices, to create device interactions, and to store data. Users can use the hosted version of ThingSpeak or setup instances on their own servers by getting the source code from GitHub. The technology behind ThingSpeak is Ruby 1.9.2, Rails 3.0, EventMachine, Phusion Passenger, Nginx, and Memcached to form a highly scalable infrastructure for the emerging Internet of Things and its data model requirements.

You use ThingSpeak to Send and Receive “data” via simple HTTP requests, much like going to a web page and filling out a form. Data can be from
anything — Blood Sugar Levels measured by a glucose meter, Server Usage and Uptime reported by servers, or Location Info from a mobile phone. Once the data is in ThingSpeak, you can build applications that retrieve the data, use the data for process decision-making, and reporting.

[via Pittsburgh Ruby Users Group]

We have created a new ThingSpeak Sketch for Arduino 1.0 that you can use for the Arduino and Ethernet Shield or the Arduino Ethernet all-in-one. All you have to do is add your ThingSpeak Write API Key to the sketch, upload to the Arduino, and connect to your network. The sketch includes automatic network configuration with DHCP, domain name resolution using DNS, a watchdog / reset function to keep the Arduino online, and a function to update ThingSpeak Channels. The new sketch has been running without hiccup in our lab for few weeks. We hope that you get the same reliability. Go ahead and copy, transform, and combine…

View Arduino 1.0 –> ThingSpeak Sketch on GitHub

Over the past few months of getting ThingSpeak to full speed, we have been inspired by the outpouring of projects and interaction with the open hardware community. So far, we announced integration with openPICUS which allows developers to create a completely open source wireless solution for the Internet of Things. There are many more announcements coming soon…

Come join us at the Open Hardware Summit!

Sponsorship opportunities are still available. Check out OpenHardwareSummit.org for more information.

[via Twitter]

Just imagine what we can learn from all of our things? Maybe we can save resources as an article by Brian McCann suggests. He also mentions connecting things to ThingSpeak as the Web of Things is being built from the ground up! Our community of developers and users are growing by leaps and bounds and we will continue to contribute to the advancement of the Internet of Things!

Brian says,

The Internet of Things refers to uniquely identifiable objects having an Internet presence. We’re not just talking about your computer, laptop, cellphone or even your TV here – we’re talking about everything. This includes your light switches, your fridge, even your toilet. With an Internet presence, all of your devices can start talking to each other and reacting to each other.

[via The Daily Gleaner]

To get the imaginations primed, the workshop is holding a Web of Things Hackathon on Saturday, June 12, 2011 at the Gray Area Foundation for the Arts. The ideas behind the hackathon are to get a bunch of people together from varied fields, focus their creative energy, and build something. The ioBridge/ThingSpeak team will be providing a complete toolkit to enable any ideas that emerge. We will have sensors, servos, Internet gateways, XBee radios, LEDs (of course), relays, microcontrollers, and early access to our next gen platform. All you have to bring is an idea… and a laptop.

If you are interested in participating, visit the Web of Things Hackathon site for more details and registration info. We look forward to working with you.

Web of Thing Hackathon [info]

Saturday, June 11, 2011 – 9am – 5 pm

Gray Area Foundation for the Arts [map]
998 Market St.
San Francisco, CA 94102

Web of Things Workshop [info]

Sunday, June 12, 2011 – 9am – 5pm

Hotel Nikko San Francisco [map]
222 Mason Street
San Francisco, CA 94102

[Elad Salomons] of OptiWater noticed that his house water pressure was 9 bars and this set him on a collision course with the Internet of Things. In his research he discovered ioBridge and ThingSpeak. He was able to connect sensors to the web, visualize the data, and come up with a few ah-ha’s in the process.

Elad is enjoying the process so much that he wanted to share the learning experience with you. He has created a contest based on some sensor data he has collected. You can look at the data and download historical data over at his Water Simulation blog to see if you can explain the correlations. You have until June 30, 2011 to figure it out. Visit Elad’s blog for more information or look him up on Twitter. $100 to learn something? That’s awesome!

[via Water Simulation / ioBridge]

[via NueWire / Arduino Forum]

All you have to do is build a project using a ThingSpeak web service, take a photo (if it applies), and writeup a description / how-to on your blog, Instructables, or email us the details. We are giving away 20 gift certificates to SparkFun valued at $50 each.

Some ideas: Use a USB data logger with ThingSpeak Importer, track a car using ThingSpeak geolocation services, create a mashup using ThingSpeak Plugins…wait…we have said too much. We want to be surprised by what you come up with, so feel free to get creative.

Disclaimer: All entries will be published on the ThingSpeak Community Blog and selection is based on meeting the described criteria. All rulings are at the final discretion of the ThingSpeak team members. Let’s see how crazy this will get!

Coming soon: We have some exciting things in store for you. Users have been asking for an index of public channels, so we are going to add a searchable project index soon. This week we will be announcing the beta release of a new application built on the ThingSpeak platform.