## 2 Related Work  
### GPSR and Its Adaptations in Dynamic Networks  
GPSR (Greedy Perimeter Stateless Routing) is a well-known position-based routing protocol, originally designed for vehicular ad hoc networks (VANETs) due to its scalability and low communication overhead [13; 22; 23; 24]. However, when applied to flying ad hoc networks (FANETs), GPSR faces significant challenges caused by three-dimensional high-speed mobility, frequent link breaks, and highly dynamic topologies. The greedy forwarding strategy of GPSR tends to fall into routing voids, thus compromising delivery reliability and network robustness. To address these issues, recent studies have focused on enhancing link stability and improving the resilience of GPSR-based routing.  
In [25], [PERSON] et al. proposed the OLSR+GPSR protocol, combining the topology-driven OLSR with the location-driven GPSR. A fuzzy control system was used to dynamically adjust the HELLO message interval based on node velocity and positional prediction error, thereby improving link awareness. Moreover, the protocol integrates multiple metrics, including neighborhood density, velocity vector, buffer occupancy, and residual energy, into the MPR (Multi-Point Relay) selection process. Although the scheme enhances OLSR's applicability to FANETs, it still requires partial topology maintenance and lacks predictive capability regarding future topology changes.  
To address multipath diversity and load balancing, the AM-GPSR protocol [26] introduces an adaptive HELLO mechanism and a multipath greedy forwarding scheme. By adjusting the HELLO interval according to neighbor stability and maintaining multiple candidate paths with priority ranking, the protocol provides better resilience to link failures and improves balancing. However, the additional control overhead and reliance on single-hop information limit its scalability in large-scale UAV swarms.  
SZLS-GPSR [27] introduces the concept of a \"communication safe zone,\" restricting candidate next-hop nodes to a stable sub-region within the communication radius. By jointly considering direction projection length, cumulative link duration, and movement direction within the zone, it significantly reduces the probability of falling into routing voids and improves packet delivery success. Nevertheless, this approach focuses mainly on local stability and does not exploit trajectory information to improve global topological awareness.  
In UF-GPSR [28], [PERSON] et al. introduce a utility function in the greedy forwarding process, which integrates residual energy ratio, distance reduction, movement direction, link risk, and average velocity to evaluate next-hop suitability. This enables more accurate and robust forwarding decisions.  
In high-density networks, CF-GPSR [29] utilizes a cylindrical filtering method to constrain candidate nodes within a 3D cylinder between the source and the destination. Combined with metrics such as velocity alignment, ideal distance, residual energy, and movement angle, the protocol builds a composite utility function that enables low-overhead and reliable next-hop selection.  
Overall, current GPSR adaptations improve performance in dynamic UAV networks by enhancing greedy forwarding with energy awareness, link stability, and multi-factor evaluation. However, most methods still rely on real-time single-hop neighbor information, lacking the ability to anticipate future topological changes. Moreover, few approaches have integrated machine learning-based decision-making to construct truly adaptive routing protocols.