As with any application, with intelligent edge solutions, the output depends on the input. The problem with a lot of locally generated input is that the quality can be very variable. Examples include camera images that become blurred due to water vapour or sensors that produce many outliers in their measurements.
You also have to be careful with self-learning algorithms that form a black box, says Robbrecht. "As I emphasized earlier, you must still be able to explain how the algorithm arrives at a certain outcome. If you make everything smart, devices can start to work against each other. 'Dumb' devices have predictable behaviour via a clear if-then-else decision tree. If many more devices start working smartly and are therefore less predictable, this could also have the opposite effect. Consider smart energy devices in combination with an energy contract with flexible prices. There is a chance they will all switch on at the same time, exceeding your grid connection limit. If all companies in the area react the same, this could even lead to serious disruptions in the network."
Another point of attention are the unhappy flows. "It's generally not that difficult to fully automate a happy flow. But does the algorithm also make the right decisions in the event of a very rare unhappy flow? And if a wrong decision is made, what are the consequences? Especially when it comes to automatically intervening in physical processes, things can go very wrong with unhappy flows. If you don't think through these processes very well, you simply run great risks. After all, risk is chance times impact", Robbrecht warns.
Securing the edge computer can also be an issue. After all, it is often located in a cabinet somewhere in a factory hall, or even in a cabinet somewhere in the open field. "Physically, an edge computer like this is not easy to secure; you can just walk up to it, attach a device to it and take over a large part of the operational systems. The starting point should always be that every edge device must be treated as a potentially unfriendly guest in the network. A guest who must continuously prove that his behaviour is safe. Sometimes things go wrong unintentionally, like the edge computer on a ship that had a USB port. The employee on the ship used that port to charge his phone, resulting in a virus contamination in the primary process", says Robbrecht.
A final point of interest he brings to our attention is what happens to the data and algorithms when the device is sold. "If you sell your Tesla, what happens to the data that's still on the edge? You need to legally record these things. For example, think of a business unit that is being sold, but you don't want your IP to suddenly become part of that deal", Robbrecht warns.Â
