# Routing Schemes in FANETs: A Survey
## 6 Open Issues
### Performing Routing in the Multi-UAV Swarm Scenarios  
In the multi-UAV swarm scenarios, a massive amount of data is generated by a large collection of autonomous, small-sized, and lightweight (or non-weight-carrying) UAVs, which is a scenario called ultra-densification. Multi-UAV swarm also possess unique characteristics of FANETs, such as high mobility degree and high dynamicity of network topology. In addition to high dynamicity (X.1) of network topology, high cost (X.2), and low residual energy (X.3), there are two new challenges: (a) high channel demand requires high channel capacity [135; 136], and (b) high collision rate among UAVs in a large swarm of UAVs requires reliable communication [137]. Further investigation could be pursued to investigate routing for reliable communication under different mobility models with different social behaviors of swarms (e.g., a school of fish, a swarm of bees, a swarm of ants, and a pack of wolves), levels of ultra-densification, and collision rates. Further investigation could also be pursued to design context-aware routing schemes that select next-hop and backhaul nodes (or BS) based on internal and  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Open Issue** & **Purpose** & **Challenges** & **Proposed Solutions** \\ \hline Minimizing the effects of frequent link disconnections to improve routing & Reducing packet retransmission and reestablishing routes & \(\bullet\) High dynamicity & Predicting the next geographical location & Predicting the UAV in route selection and maintenance. \\ \hline Performing routing in the multi-UAV swarm scenarios & Managing massive amount of data due to ultra-densification & \(\bullet\) High dynamicity & Predicting the next geographical location & Predicting the next geographical location \\ \hline Performing clustering for supporting routing in multi-UAVs & Deploying collaborative tasks, including data aggregation, load distribution, and resource distribution & \(\bullet\) High cost & Predicting the next geographical location & \\ \hline Enhancing mobility models for the investigation of routing in FANETs & Mobility management & \(\bullet\) High dynamicity & \(\bullet\) High cost & Using context-aware approaches, such as artificial intelligence approaches and bio-inspired algorithms \\ \hline Improving performance survivability multi-pathing & network resource utilization and reducing network congestion & \(\bullet\) High dynamicity & Forming mobility models based on real-life scenarios & \\ \hline Improving network performance by using artificial intelligence & Optimize performance & \(\bullet\) High cost & \(\bullet\) High cost & Using AI approaches to improve network performance \\ \hline Improving network coverage by using high and aerial vehicles (UAVs) & Reducing packet retransmission and reestablishing routes & \(\bullet\) High cost & Enabling collaboration between high and low altitude platforms \\ \hline Reducing consumption green energy & Reducing network partitioning & \(\bullet\) High cost & \(\bullet\) Use solar panels for extra energy backup \\ \hline \hline \end{tabular}
\end{table}
Table 5: A Summary of open issues and their purposes, challenges, and proposed solutions.  
external ambient factors, such as geographical location, the amount of fuel, and hardware capabilities, in order to establish long-term routes [93].