Pipe Routing with Topology Control for UAV Networks

TL;DR

This work tackles unstable routing in dynamic, decentralized UAV networks by proposing Pipe routing, a mobility-, congestion-, and energy-aware hybrid reactive protocol that maintains a pipe around active routes and proactively switches to better routes within it. It further strengthens reliability with TC-Pipe, a pheromone-based topology-control scheme that densifies the pipe vicinity to sustain high-quality alternate routes. Through simulations across single and multi-target scenarios, Pipe and especially TC-Pipe achieve higher PDR and longer route uptime with fewer route breaks than AODV and ConCov-Pipe, at the cost of modest coverage trade-offs that diminish at higher node densities. The results demonstrate practical improvements in throughput and stability for target-monitoring UAV swarms, with Relay serving as a performance bound for routing robustness versus coverage preservation.

Abstract

Routing protocols help in transmitting the sensed data from UAVs monitoring the targets (called target UAVs) to the BS. However, the highly dynamic nature of an autonomous, decentralized UAV network leads to frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and/or incur large control overhead and delays. To establish stable, high-quality routes from target UAVs to the BS, we design a hybrid reactive routing scheme called pipe routing that is mobility, congestion, and energy-aware. The pipe routing scheme discovers routes on-demand and proactively switches to alternate high-quality routes within a limited region around the active routes (called the pipe) when needed, reducing the number of route breaks and increasing data throughput. We then design a novel topology control-based pipe routing scheme to maintain robust connectivity in the pipe region around the active routes, leading to improved route stability and increased throughput with minimal impact on the coverage performance of the UAV network.

Figures (27)

• Figure 1: Decentralized, autonomous UAV network performing monitoring, search and surveillance in inaccessible disaster areas.
• Figure 2: Illustration of a pipe around the active route (red links) from the target UAV to BS. The pipe consists of nodes (green nodes) that are up to 2-hop from the nodes along the active route (red nodes).
• Figure 3: Illustration of pipe thinning problem, where the node $N'$ has no 1-hop neighbors except the upstream and downstream nodes on the active route.
• Figure 4: Applying a pheromone mask to attract UAVs.
• Figure 5: The target locations in a 6x6 $km^2$ map. Three different target locations are shown for a single target in (a), (b) and (c) and for a group of 3-targets in (d), (e) and (f).