Connecting the road with Octoblu, IBM and Local Motors
Imagine this scenario a few years from now. A construction manager is driving to work. His job is pretty dangerous, so he asks the car, “What does my day look like?” The car replies, “High likelihood of accident on your current route: rerouting. Also, might I suggest suspension of work activity from 2:31 PM until 3:26 PM in Zone Three due to a combination of weather forecast and pedestrian traffic analysis? I’ll update your task lists and notify all personnel.” Upon arriving at the construction site, the vehicle parks itself into a charging station and readies itself for its next passenger. The vehicles are printed en masse, so everyone shares them, and through proximal identification, robotic drivers customize themselves to their passengers.
Although this scenario probably sounds a bit too much like science-fiction, this future isn’t far off. Recently, Joe Speed of the IBM Connected Car Project reached out to get some help bootstrapping the latest 3-D printed concept car from Local Motors with connected features using Octoblu, Node-Red and IBM IoT for Automotive. So I spent the next week and a half with IBM, Local Motors and Link Motion to get the car ready for its unveiling at IBM Insight 2015.
Moheeb Zara and Jeff Harris
What we have in the car now is the foundation for building a future where the preceding scenario is a reality. Great innovation is often the product of combining powerful elements in a way that complements the qualities of each component. So when you take Octoblu—an infrastructure that enables integration across any device, service, protocol or platform—and combine it with IBM’s myriad solutions for analytics and machine learning, and integrate it into the Local Motors 3-D printed electric vehicle, the result is a powerful platform upon which to develop the future of intelligent vehicles.
Still not convinced that a future like I described is coming any time soon? Let’s go over what the car is currently capable of and where it's headed. At Octoblu, we’ve been building an all-encompassing, all-inclusive and primarily open source infrastructure for the Integration of Everything (IoE). The IoE might sound like a buzzword, but it’s a mantra that guides everything we do. With the onslaught of new devices and platforms being produced in the Internet of Things (IoT) market, it becomes increasingly important to ensure connectivity with all platforms—even those that compete. When the eventual goal is a connected future where data from sensors and systems all over the world have to work together to produce meaningful data, it is detrimental to create silos and closed off systems. Octoblu provides an open source, multi-protocol machine-to-machine messaging platform with a device registry (Meshblu) along with an application programming interface (API) and device management solution that includes a visual designer for building cloud applications (Octoblu). This allows us to connect anything and everything as long as it can access a public/private Meshblu or Gateblu, our gateway software for integrating local devices. With this now integrated into the concept car with a rolling mesh, we can connect anything we want and continue to extend the features of the vehicle.
Octoblu also makes it easy to collect data from any source, whether web service or device, and forward it into IBM’s resources for analysis. Sensor networks such as Libelium hubs (used in the Barcelona Smart City Project) can forward data to the IBM Cloud through Octoblu for analysis alongside meteorological data, social media, smart car positioning, traffic data and much more. This is how we can achieve a reliable predictive solution for our future car scenario.
The current working demo shows how we’re using Node-Red, a low-cost embedded platform such as a Raspberry Pi, IBM MQTT and Meshblu to interface data to and from the cloud. The Link Motion infotainment system in the dash shows vehicle information as well as a live map of its location. The IBM IoT for Automotive web page remotely displays this information in real time, as well as a map of all vehicles connected to the cloud. Individual vehicles or clusters on the map can be sent alerts regarding hazards. Lights in the car and notifications on the Link Motion relay these alerts. A local Meshblu instance interfaces all control and data to any platform through Octoblu.
Going back to the scenario, let’s check off plausible items on our sci-fi checklist:
- Predictions based on big data: IBM
- Integration with services and sensors (tasks, SMS): Octoblu
- 3-D printed electric vehicle: Local Motors
- Proximal identification: Octoblu
So we don’t have self-driving yet; we’ll leave that to DARPA and Tesla. However, this is a young project and we’re just getting started. There is a lot of experimenting to do and I’m excited to see where it leads. So stay tuned; this partnership is going to yield some interesting results as we venture into the open source realm of smart vehicles!