I’m happy to announce that we’ve hit a special milestone for Sensorpedia. We now have over 3,000 individual sensors and data sources interfaced with the framework! (3,298 as of Thursday morning July 30th to be exact.) A big thanks to our beta testers and summer interns for all their contributions.

Sensorpedia now contains a wide variety of sensor information. The following tag cloud gives you an idea of what types of data you can find. Much of the data is weather related, coming from International Civil Aviation Organization (ICAO) and National Oceanic and Atmospheric Administration (NOAA) weather stations and buoys. We also have a number of traffic cameras from around the Southeast, including a concentration of cameras in Charleston, SC (thus it’s prominence in the tag cloud). Other important sensor types monitor seismic activity, energy efficiency, and water levels. Then there are the more obscure sensors like those used for monitoring of bacteria level at beaches. The greatest concentration of sensors is still in the United States, but several sensor systems are also included from Europe, Asia, and other parts of the world!

See our Sensorpedia Flickr Group for screenshots of what 3,000 sensors looks like in the Sensorpedia web application.

What sensor data will we be adding next? Maybe it’s yours! If you have sensor data that you’d like to incorporate into the Sensorpedia beta, please contact us.

My mouth is going dry, my mind is leaving, I’m beginning to sweat… I must be filming the Sensorpedia video blog…

Here is the outtakes version:

Trouble seeing the video? View Sensorpedia: Who and Where (Outtakes) on YouTube.

Have you ever visited the website of a company or organization but still just couldn’t quite get a “feel” for what the place or the people there were really like? Perhaps you couldn’t visualize the physical layout of the campus, or maybe the people mentioned didn’t seem “human” in the context of cyberspace. Or was it that you could not sense the organization’s history and how it came to be as it is today? I’ve oftentimes felt these things too. So, as promised in an earlier blog post, I recently shot a video blog to answer these types of questions about myself, Sensorpedia, and the Oak Ridge National Laboratory. Pay special attention to the first-time-ever-revealed interests of my mentor, David Resseguie.

Trouble seeing the video? View Sensorpedia: Who and Where on YouTube.

Update: The Video Blog Outtakes is now on YouTube too!

Today I’d like to introduce the first iteration of a python library written to ease the process of generating ATOM feeds for Sensorpedia.  With spylib, no knowledge of XML or ATOM is required to publish a sensor group, though an understanding of the various tags and what Sensorpedia wants to do with each of them is advised. (The definitive source of this information can be found in the Sensorpedia API.)

Creating a sensor registration document is easy as Py:

myfeed = sensor.SpGroup("efa6e019-eca4-4fd6-a37b-c57ad2b1f441")
myfeed.title = "Cool Sensors near Akron, OH"
myfeed.georss_point = [41.090737,-81.496582]
 
mysensor = sensor.SpSensor("efa6e019-eca4-4fd6-a37b-c57ad2bFALLS", title = "Cuyahoga Falls, OH")
mysensor.georss_point = [41.166441,-81.536858]
 
myfeed.addsensor(mysensor)
atomtext = myfeed.toprettyxml()

Some Quick Links:

• Project Home Page
• API Reference

In this blog post, we’ll discuss the rationale behind the library, and provide a tutorial.
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