Using Autonomous Vehicles to Explain IIoT

Blog Post created by arcadvisory on Oct 3, 2016

By Will Hastings

When attempting to explain the Industrial Internet of Things (IIoT) to the unacquainted, I frequently rely on the autonomous vehicle as an example, and it works.  While the topic is great for catching the attention of the audience, it also demonstrates IIoT-type solutions for problems to which everyone can relate.


I’m not referring to the current generation of cars with self-driving capability.  I admire companies like Tesla and Google for advancing the technology, but the short-term benefits are trivial in comparison to the longer term potential.  These advances are a necessary step in the right direction; however, they lack connectivity.  Connectivity will revolutionize the transportation industry.


Sensor data, Big Data analytics, machine learning, and machine-to-machine (M2M) communication are imperative for the successful application of IIoT philosophy.  Right now most autonomous vehicle projects utilize sensor data, analytics, and, to some extent, machine learning.  Currently, there are not enough autonomous vehicles on the road to make M2M a viable tool.  However, once a critical mass is met, autonomous car control can enable autonomous traffic control and this is where things get interesting.


Such a system will have an extraordinary influence on industries and society as a whole.  A list of more obvious benefits includes the reduction of travel time in congested commuter areas and cities, a substantial reduction in accidents and accident-related injury or death, and an increase in community fuel economy.  Taking our vision one step further, we can imagine a situation where owners rent out vehicles as autonomous taxi services while they are at work or sleeping.  What happens to an economy when these liabilities become assets and no longer sit idle for hours at a time?  On a similar note, how will freight services change when trucks stop only to pick up, drop off, and refuel?  What will happen to the commercial airline industry when people can travel while sleeping in their cars?


The problem with this vision is in its implementation.  There are social, political, and economic hurdles that will prevent such a scenario from unfolding in the near future, but that doesn’t mean it isn’t a perfect example of IIoT.  There are easily identifiable problems that require complex solutions to solve; implementing sensors, analytics, and M2M communication.  These issues can be compared to similar concerns in industry: real-time traffic control is analogous to process control and predictive analytics, ride sharing systems can be compared to inventory or asset management, and collision avoidance and autonomous routing can be seen in warehouse robotics today.  There are also major opportunities that are not apparent and require serious investigation to be understood.


The autonomous vehicle is such a powerful example because everyone experiences the issues that it could solve on a daily basis.  The opportunities in other industries may not be as clear to the average person, but, unlike our example, forward-thinking companies are addressing these opportunities today.