Google Spreadsheet

Temboo ignited with a Spark

Posted by kkaiser

This is a re-post of an article I wrote as a guest blogger for Temboo’s blog.
I am working with connected devices and was looking for a cloud service. While surveying the field Temboo caught my eye because of the large number of supported premium web sites and the promise to connect IoT devices to the Internet in a breeze.
spark_size The connected device I am using is a Spark Core. The Spark Core is a sleek small board that offers a powerful 32 bit ARM CPU paired with WiFi. The product grew out of a Kickstarter campaign and is rapidly gaining in popularity. The Spark is nicely priced and everything is open source. The team supporting the Spark Core is smart and supportive and made a great choice to port most of the main Arduino APIs to their platform.
As outline in a blog post here migrating Arduino Libraries to the Spark Core often turns out to be pretty easy. With Temboo providing an open source library for Arduino I was tempted to give it a try. However, I had no Temboo-Arduino setup so I was not sure how hard it would be to get it all up and running.
Well, I am happy to report that is was easier than expected. Temboo’s code is well written. I only had to work around some AVR specific optimizations that Temboo did to save program memory. As the Spark Core is built around a STM32F103 chip resources are not as tight as with the AVR so I simply bypassed these optimizations.
Here are some brief instructions how to install the Temboo Arduino Library. The instructions use the Spark command line SDK setup:

  1. Download the modified Temboo Arduino Library source code from github: 
    mkdir temboo
cd temboo
git clone https://github.com/bentuino/temboo.git
  2. Get the Spark Core firmware: 
    git clone https://github.com/spark/core-firmware.git
git clone https://github.com/spark/core-common-lib.git
git clone https://github.com/spark/core-communication-lib.git
// Merge the two source codes
cp -fr core-* temboo
rm -fr core-*
  3. In older Spark firmware there is a small problem that the spark team already fixed. Open the file core-firmware/inc/spark_wiring_ipaddress.h and uncomment the line 54 with your favorite editor: 
    // Overloaded cast operator to allow IPAddress objects to be used where a pointer
// to a four-byte uint8_t array is expected
//operator uint32_t() { return *((uint32_t*)_address); };
bool operator==(const IPAddress& addr) { return (*((uint32_t*)_address)) == (*((uint32_t*)addr._address)); };
bool operator==(const uint8_t* addr);
  4. Save your TembooAccount.h you generated with DeviceCoder to temboo-arduino-library-1.2\Temboo
  5. Now it is time to build the Spark application: 
    cd temboo/core-firmware/build
make -f makefile.temboo clean all
  6. Connect your Spark Core to your computer via a USB cable
  7. Push both buttons, release Reset button and continue holding the other button until RGB-LED lights up yellow
  8. Download application into Spark Core 
    make -f makefile.temboo program-dfu

Temboo Examples

Two simple Spark application examples are included:

  • core-firmware/src/application_gxls.cpp – Example demonstrates the Temboo library with Google Spreadsheet
  • core-firmware/src/application_gmail.cpp – Example demonstrates the Temboo library with Gmail

to change the example that is built, edit the first line in the core-firmware/src/build.mk file:

CPPSRC += $(TARGET_SRC_PATH)/application_gxls.cpp

or

CPPSRC += $(TARGET_SRC_PATH)/application_gmail.cpp

Building this code was tested under Windows 8.1 using cygwin and the MINGW version of the ARM GCC compiler tool chain. It should be easy to use this Temboo Library with the Spark Cloud based SDK. To configure the Library to support Spark all the is required is to define the following label:

CFLAGS += -DSPARK_PRODUCT_ID=$(SPARK_PRODUCT_ID)

or add a

#define SPARK_PRODUCT_ID SPARK_PRODUCT_ID

to the source code. Temboo support for the Spark Core is a lot of fun. It is easy to setup your own Temboo account and compile the Temboo Arduino Library that now supports the Spark Core platform. To learn more about similar projects please visit my blog at http://bentuino.com.

Google polls Spark Core

Posted by kkaiser

appsIn a previous blog post I was describing an example of how a Spark Core can be used to read weather sensors. The setup was really no different from any simple Arduino Uno setup. It only demonstrated how easy it is to port Arduino Sketches to a Spark Core.
With the integrated WLAN I was interested to connect the Spark Core to the internet cloud. One of the simplest ways I found, was using Google’s Spreadsheet service. I stumbled over this idea in this Spark forum post.
Here is how it works: a Google Script is periodically reading data from the Spark Core via the RESTful Spark API and then appends the data to a Spreadsheet. The code below is a minimalistic Spark code to test the such a setup:

int variable = 1634;
void setup() {
  Spark.variable("variable", &variable, INT);
}
void loop() {
  variable++;
  delay(5000);
}

It publishes a variable for cloud access and then increments it in regular intervals. Together with the following Google script I was able to quickly pull data from my core.

function collectData() {
  var  sheet = SpreadsheetApp.getActiveSheet();
  var sensor = UrlFetchApp.fetch("https://api.spark.io/v1/devices/<id>7/variable?access_token=<token>");
  // parse the JSON the Core API created
  var sensor = JSON.parse(sensor.getContentText());
  // you'll need to unescape before your parse as JSON
  var sensor_result = unescape(sensor.result);
  // create a time stamp
  var d = new Date();
  // append data to spreadsheet
  sheet.appendRow([d, sensor_result]);
}

However when I setup a time trigger to run the script in regular intervals I found the setup to be very unreliable. This is discussed and documented by several Spark Users and as of this writing I have not seen a fix for this problem.
One thing to note is, that this approach is pulling data from the Spark Core rather than the core pushing them to the cloud. This has a significant flaw as we cannot put the core into standby between the measurement intervals. Therefore this solution is anyway not a good choice for low power applications.
So stay tuned, I am experimenting with a better solution that I will blog about in my next post.