AxisGeo: proactive axis-guided 3D geographic routing with local refinement and void avoidance in flying Ad hoc networks

Abstract

Flying ad hoc networks (FANETs), based on unmanned aerial vehicles (UAVs), can provide innovative services in dynamic and distributed environments. However, their three-dimensional and highly mobile nature causes unstable links, rapid topology changes, and routing voids, which can degrade the performance of conventional routing protocols. This paper proposes a new proactive axis-guided 3D geographic routing approach (AxisGeo) for FANETs. It is fully distributed, maintains no routing tables, and relies only on one-hop neighbor information. AxisGeo employs two complementary geometric structures– the compressed global axis and the zoomed local axis– to adaptively guide packet forwarding. The global axis provides a fixed source-to-destination direction; whereas the local axis is proactively refined via an attraction-repulsion mechanism, where neighbor-quality information (residual energy, connectivity, progress toward destination, and expected transmission counts (ETX)) is transformed into geometric adaptation forces rather than being used directly to rank next-hop UAVs. Therefore, the proposed mechanism refines the local forwarding geometry itself before node selection. The mechanism relies only on one-hop information and requires no additional control messages beyond periodic beacon exchanges. To address routing voids, AxisGeo employs a NACK-based lightweight self-repair mechanism to reconstruct broken paths. This protocol assumes that UAVs are equipped with positioning systems and periodically exchange beacon messages to maintain one-hop neighbor information. The performance of AxisGeo was evaluated against A-Geo, UF-GPSR, and GPSR under three simulation scenarios involving variations in UAV density, UAV speed, and network traffic. Under varying node density, the packet delivery ratio (PDR) increased from 75% to 96%, end-to-end delay decreased from 190 ms to 78 ms, and throughput improved from 38.6 kbps to 52.1 kbps, corresponding to average performance improvements of 4.41%, 12.10%, and 6.77% over the strongest benchmark (A-Geo), respectively. When varying UAV speed, PDR changed from 94% to 78%, delay increased from 104 ms to 138 ms, and throughput decreased from 48.6 kbps to 39.2 kbps, with corresponding average improvements of 3.53%, 1.53%, and 2.27% over the best baseline (A-Geo). Under varying network traffic, PDR changed from 97% to 87%, delay varies from 99 ms to 147 ms, and throughput increased from 32.1 to 67.9 kbps, achieving average improvements of 2.24%, 2.98%, and 9.53% relative to the strongest benchmark (A-Geo). These results indicate that AxisGeo provides improved routing performance across the evaluated FANET scenarios.