  Provenance embedding algorithms are well known for tracking the footprints of
information flow in wireless networks. Recently, low-latency provenance
embedding algorithms have received traction in vehicular networks owing to
strict deadlines on the delivery of packets. While existing low-latency
provenance embedding methods focus on reducing the packet delay, they assume a
complete graph on the underlying topology due to the mobility of the
participating nodes. We identify that the complete graph assumption leads to
sub-optimal performance in provenance recovery, especially when the vehicular
network is sparse, which is usually observed outside peak-hour traffic
conditions. As a result, we propose a two-part approach to design provenance
embedding algorithms for sparse vehicular networks. In the first part, we
propose secure and practical topology-learning strategies, whereas in the
second part, we design provenance embedding algorithms that guarantee
ultra-reliability by incorporating the topology knowledge at the destination
during the provenance recovery process. Besides the novel idea of using
topology knowledge for provenance recovery, a distinguishing feature for
achieving ultra-reliability is the use of hash-chains in the packet, which
trade communication-overhead of the packet with the complexity-overhead at the
destination. We derive tight upper bounds on the performance of our strategies,
and show that the derived bounds, when optimized with appropriate constraints,
deliver design parameters that outperform existing methods. Finally, we also
implement our ideas on OMNeT++ based simulation environment to show that their
latency benefits indeed make them suitable for vehicular network applications.
