Towards Accurate and Low-Cost Path Reconstruction in Mobile UAV Networks

Due to the maneuverability and cost-effectiveness, UAVs are visioned as a versatile assistant for networking in intelligent transportation systems. Per-packet path reconstruction that reveals the multi-hop forwarding path of each packet is a fundamental service for network management and optimization. Unfortunately, most existing reconstruction schemes are designed for static networks with stationary devices which cannot be applied to UAV networks with strong mobility and frequent topology changes. Besides, the limited energy and resources of UAVs make it even more challenging to reconstruct dynamic paths. To achieve accurate and low-cost reconstruction, we propose an
in
cremental
p
ath
rec
onstruction (InPrec) for dynamic UAV networks. With InPrec, each UAV only records the changes of its one-hop neighbors in a distributed and change-driven manner. The sink UAV then incrementally recovers the full path information by replaying the recorded changes to the historical paths. In this way, InPrec can adapt to the topology changes and avoid repetitive records of the common sub-paths in dynamic UAV networks. To explore the pros and cons of InPrec, we conduct measurement studies and theoretical analysis from perspective of information entropy. Simulations also demonstrate that InPrec can accurately reconstruct the paths with time-varying topology and reduces much redundancy (by 85% on average) of the state-of-the-art work. As far as we know, this is the first path reconstruction approach applicable to large-scale dynamic UAV networks.