An international standard data model for radio frequency emissions is in the works and welcomes your input.
Today companies that model the properties and behavior of RF signals in natural, urban and industrial settings use databases to organize environmental RF data. Their data schemas are based on abstract data models. Different companies develop different abstract data models to suit their particular needs.
That's fine as long as there is no requirement to share their data with others who use other software or to integrate it with other data from other sources. It's fine as long as there are software providers willing to write interfaces for multiple data models.
There are benefits, however, for both providers and users of such services if there were a standard abstract data model and derived encoding for electromagnetic field data. Technology providers would have more potential customers and customers would have more choices.
I'm a member and former staff member of the Open Geospatial Consortium (OGC). The OGC is a standards organization founded to help break up silos of geospatial data.
As soon as digital geographic information systems (GIS) and Earth observation systems first came into use half a century ago, different types of spatial/temporal data (images, vectors, point networks, etc.), different coordinate reference systems, different vendor formats and different user data models began obstructing organizations' efforts to share, reuse, aggregate, compare, and analyze their data. Now, after 22 years of OGC consensus standards work, in many of the domains that depend on spatial/temporal data – defense, climate, hydrology, geology etc. – data sharing and integration are much easier. Cross-domain data integration is also easier.
Wireless communication depends on RF spectrum, a unique natural resource that is endlessly reusable but strictly limited in any place in any fraction of a second. Continued growth in mobile devices, smart vehicles and the Internet of Things depends critically on our ability to maximize spectrum utilization and minimize interference.
Industry maximizes spectrum utilization through clever manipulation of frequency, amplitude and polarity. The next step is dynamic real-time "cognitive" management of frequency, amplitude and polarity. This becomes ever more complex and dependent on both institutional coordination and real-time automated coordination.
In the IoT, the need to minimize interference is heating up, literally, in two ways.
Cross-talk between similar and adjacent systems in smart buildings poses risks to life and property. Secondly, random electronic pulses become more likely to damage the IoT's electronic devices as the devices miniaturize. Pulses come not only from utility infrastructure, electric cars and other artifacts of our increasingly electrical world, but also from signals and active sensing emissions.
The FCC recently initiated a Noise Floor Inquiry to determine if there is an increasing noise problem, and, if so, its extent. The National Academy of Sciences last year published A Strategy for Active Remote Sensing Amid Increased Demand for Radio Spectrum. (Terrestrial RF sometimes interferes with Earth observation.) Both initiatives would benefit from a standard RF data model.
We know we can't manage what we can't measure. We tend to forget that we can't measure everything ourselves, so we need standards that enable us to share and integrate our data.
--Consultant Lance McKee was on the startup team of the Open Geospatial Consortium in 1994 and now chairs the OGC Spectrum Model Language Domain Working Group.