1. All Commercial IoT Devices Must Go Through Rigorous Regulatory Certifications
If you’re going to develop and commercialize your own IoT device, it must be certified for RF operations in the respective market(s). Regulatory testing ensures the successful co-existence of IoT devices for minimized interference in the shared radio spectrum. Unfortunately, there’s no universal certification body with globally recognized testing standards; instead, each country or region has its own regime and accountable organization. For example, you need the FCC certification in the USA, the IC in Canada and the CE in Europe.
Many companies undermine how demanding and time-consuming the certification process can be. Testing must be done in an accredited test lab and may be required for individual components as well as the entire product. Preparing and filing out the necessary paperwork can take weeks or even months, not to mention the expensive testing costs. As a simple example, the FCC certification for RF devices generally includes two levels: the General Emission testing at an expense of up to $5,000 and the more complex Intentional Radiation testing which can cost up to $15,000. If your device operates in different regions, you’ll have to make sure it gets all applicable certifications.
The best way to minimize the significant costs and burden of regulatory testing is to plan for it from day one of your product design and select “pre-certified” device components. Typically, pre-certified RF transceivers have already passed through CE and FCC testing and can save you the most cumbersome part of your device certification. Going for such components also minimizes the risk of designing a non-compliant product, as well as unnecessary expenses and delays due to re-testing.
2. Edge Intelligence Isn’t Married to IoT Devices
With the exploding data volumes in the IoT era, edge computing has gained significant traction. The conventional model of processing and storing all data in the cloud or a data center has proved to be costly and inefficient. One main reason is that the majority of telemetry data is often irrelevant and doesn’t need to be transferred to the cloud. For example, a status message might be useful only if it informs you of something abnormal. Such a centralized approach also places undue pressure on the data infrastructure while causing unnecessary latency in time-critical applications.
That’s why edge computing, or edge intelligence, which refers to the practice of locally processing data near its source, is becoming more and more important. While it’s easy to relate edge intelligence as a function of end devices at first sight, it’s rarely the case.
Consumer IoT products are often sophisticated, but in industrial and commercial contexts, a larger number of connected devices are small, battery-powered sensors with very low computing power. With a network of hundreds or even thousands of data points, you simply can’t afford to have high processing capability on every single device.
Instead, the intelligence is pushed to an IoT gateway or industrial PC that aggregates data from numerous end points. These local data hubs process only relevant information, before pushing it to a central infrastructure like the cloud. Leaving heavy computing tasks to an edge gateway rather than on each device enables a more streamlined architecture that reduces cost and complexity.