Abstract:
Unmanned Aerial Vehicles (UAVs), or drones, are increasingly expected to operate in spaces
populated by humans while avoiding injury to people or damaging property. However, incidents and
accidents can, and increasingly do, happen. Traditional investigations of aircraft incidents require on-board
ight data recorders (FDRs); however, these physical FDRs only work if the drone can be recovered.
A further complication is that physical FDRs are too heavy to mount on light drones, hence not suitable for
forensic digital investigations of drone ights. In this paper, we propose a self-adaptive software architecture,
LiveBox, to make drones both forensic-ready and regulation compliant. We studied the feasibility of using
distributed technologies for implementing the LiveBox reference architecture. In particular, we found that
updates and queries of drone ight data and constraints can be treated as transactions using decentralised
ledger technology (DLT), rather than a generic time-series database, to satisfy forensic tamper-proof
requirements. However, DLTs such as Ethereum, have limits on throughput (i.e. transactions-per-second),
making it harder to achieve regulation-compliance at runtime. To overcome this limitation, we present a
self-adaptive reporting algorithm to dynamically reduce the precision of ight data without sacri cing the
accuracy of runtime veri cation. Using a real-life scenario of drone delivery, we show that our proposed
algorithm achieves a 46% reduction in bandwidth without losing accuracy in satisfying both tamper-proof
and regulation-compliant requirements.