Think for example of the following possible scenarios:

1. Car2X communication: How should cars communicate with the environment and peer cars? How can an open distributed ledger help by saving and sharing information between cars like location, speed, direction etc. to foster smart mobility?
2. Autonomous, self-administrating trucks: once the truck is autonomous it will need to have its own digital identity and an eWallet: It will perform (micro-)payments for fueling, charging, toll etc. But how can the truck decide which business partner is trustable? And when it comes to platooning, how can trucks find a platoon and how can we make sure that benefits in terms of fuel savings are allocated fairly among platooning partners?

Think of these two possible concrete use cases:

1. Tokenization of private placements: programming of a digital loan agreement/platform for private placements including fully automatic settlement and execution of payments. The number of investors at the beginning of the distribution is typically undefined and could be large (up to 500).
2. Automatic trade receivables management: In a digital ecosystem in which all sales are made online, a sale should be accounted, a bill/notice to pay should be sent and services like the financing of receivables could be done automatically. We want to automate the process of receivables financing i.e. the sale of a trade receivable and the payment should be automatically executed when the claim is accrued.

Think about this concrete use case:

1. Trust of origin and trust of accuracy: A lift bridge operator collects data from> 100 sensors. These sensor data are sent to the manufacturers of the components of the lifting bridge. Manufacturers run their predictive maintenance analysis on the data and as a result, it is possible to give a statement about the failure probability and duration of the components. This statement is then handed over to an insurer of infrastructure companies. The insurer then assesses when maintenance measures and the associated failure of the lifting bridges can make the most sense. The challenge here is to know when the data comes from a specific provider or device (trust of origin) and when the data we have is reliable (trust of accuracy).