  We introduce a permissioned distributed ledger technology (DLT) design for
crowdsourced smart mobility applications. This architecture is based on a
directed acyclic graph architecture (similar to the IOTA tangle) and uses both
Proof-of-Work and Proof-of-Position mechanisms to provide protection against
spam attacks and malevolent actors. In addition to enabling individuals to
retain ownership of their data and to monetize it, the architecture also is
suitable for distributed privacy-preserving machine learning algorithms, is
lightweight, and can be implemented in simple internet-of-things (IoT) devices.
To demonstrate its efficacy, we apply this framework to reinforcement learning
settings where a third party is interested in acquiring information from
agents. In particular, one may be interested in sampling an unknown vehicular
traffic flow in a city, using a DLT-type architecture and without perturbing
the density, with the idea of realizing a set of virtual tokens as surrogates
of real vehicles to explore geographical areas of interest. These tokens, whose
authenticated position determines write access to the ledger, are thus used to
emulate the probing actions of commanded (real) vehicles on a given planned
route by "jumping" from a passing-by vehicle to another to complete the planned
trajectory. Consequently, the environment stays unaffected (i.e., the autonomy
of participating vehicles is not influenced by the algorithm), regardless of
the number of emitted tokens. The design of such a DLT architecture is
presented, and numerical results from large-scale simulations are provided to
validate the proposed approach.
