# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
## 2 Related Work  
This section analyzes the strengths and limitations of existing mobility models to highlight the need for a new model suitable for relay-based FANET environments and to establish the rationale for proposing SREM. As shown in Table 1, existing FANET mobility models are categorized into four major types, each possessing its own unique advantages and drawbacks [17]. Group-based models are well-suited for simulating scenarios in which multiple UAVs operate cooperatively in formation or swarm configurations. These models are particularly effective in FANET applications that require coordinated group behavior, such as military missions, multi-UAV reconnaissance, and collaborative area coverage tasks. The Reference Point Group Mobility (RPGM) model, as shown in Figure 1a, has each node follow a group leader with random deviations. This model is suitable for scenarios such as military operations and disaster response [18]. The Column (CLMN) model describes UAVs advancing along a reference line while oscillating or rotating around it, making it well-suited for linear-area scanning tasks such as agricultural monitoring [19]. The Exponential Correlated Random (ECR) model captures the movement of UAV swarms using an exponentially correlated probability function, enhancing collision avoidance and link stability, and it is appropriate for large-scale UAV formation [20]. These group-based models are highly effective for analyzing inter-node distance maintenance, group cohesion, and intra-group link stability [21]. They are frequently used to evaluate collective mobility behaviors or to measure communication retention within coordinated swarms. However, since UAVs move as part of a formation, it is difficult to simulate autonomous path selection or relay path alignment for individual nodes. Planned path mobility models involve UAVs repeatedly following predefined trajectories and are commonly used in applications such as reconnaissance, surveillance, environmental monitoring, and mission-oriented data collection. Since UAV movement paths are predetermined, these models offer high trajectory predictability, along with strong repeatability and spatial uniformity. As a result, they are highly advantageous for \begin{table}
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**Mobility Model Type** & **Purpose in Simulation** & **Representative Models** & **Experimental Use Cases** & **Strengths** & **Limitations** \\ \hline Group-based & Collaborative flight, swarm, leader-based movement & RPGM, CLMN, ECR & Measure group cohesion, link stability, collision avoidance & Well-suited for formation flying, robust to internal coordination & Not suitable for individual relay testing \\ \hline Planned path & Mission-oriented scanning or patrol with fixed routes & PPRZM, MG & Evaluate coverage uniformity, path efficiency & Realistic mission replication, predictable node paths & Inflexible for dynamic path reconfiguration \\ \hline Time-dependent & Realistic inertial-based directional change & GM, ST & Analyze path continuity, delay fluctuation, connectivity & Captures motion realism, useful for inertia-based aircraft & Lacks path customization for targeted relay zones \\ \hline Random & Baseline testing, exploratory mobility patterns & RW, RWP, RD & Test routing resilience, assess distribution randomness & Simple to implement, supports wide variability & Poor realism, lacks communication path alignment \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of mobility models for flying ad hoc networks. quantitative simulation experiments, such as coverage analysis, scan evaluation, and path efficiency measurement. A representative example is the Paparazzi model (PPRZM), shown in Figure 1b, in which UAVs repeatedly fly along a fixed path. This model is optimized for systematically covering specific areas [22].