# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 4 State-of-the-Art FANET
### Role-Based Connectivity and Trajectory Management  
Management of the FANET is another crucial task for efficient networking. The general objective is to minimize the intermittent disconnectivity between the nodes and therefore reduce the latency. For example, among the nodes in a FANET, some nodes may have a higher priority to be in the connectivity loop or even a node can move in a way to prevent the FANET from splitting up into multiple disconnected clusters. This requests precise role management of the UAVs in the FANET where three management options can be available as:  
1. a series of ground base-stations to command and control the UAVs,
2. some of the UAVs work as master UAVs and control others, or
3. each UAV learns how to deal with the network variations by using edge technologies such as AI and ML tools.  
Depending on a scenario or a specific application, the optimal solution might be a hybrid model where all these three options become accessible. In this regard, [21] proposed an agent-based machine for connectivity management of UAVs by introducing four states for each UAV. Using this agent-based machine, each UAV can manage its role based on the information it gets from the nearby environment. However, this model is suitable for a swarm of UAVs when the nodes are somehow connected to each other at all times. In [101], a modular relay positioning method was proposed to manage the FANET to maintain the connectivity of the UAVs during missions with the least number of nodes. Likewise, in [102], a dynamic relay selection and positioning for FANETs was proposed. Moreover, in [64], a new model of data-aided management of FANET was proposed such that sensing and communication occur on demand in response to a specific query. The model was to collect measurements from massive passive sensors while adaptively minimizing the entropy gap and scheduling UAVs to fly over sensors.  
Path planning and management is an important topic for the mobility of FANET nodes. Although in many literature surveys, the mobility of UAVs treated like nodes in MANETs or VANETs, the mobility of FANETs must be more deterministic based on the scenario specifications since, unlike nodes in MANETs/VANETs, the nodes in FANETs can freely and flexibly fly in a 3D space. Therefore, it demands a kind of application-oriented trajectory optimization for FANETs. In [103], a model was proposed to manage the radio resource among UAVs, and to optimize the trajectories of UAVs in the network by utilizing the tools of reinforcement learning. Hence, [104; 105] proposed altitude optimization models while [47; 48; 106] focused on general trajectory optimization approaches for UAVs in FANETs. In general, while UAV trajectory design and route planning are crucial to improving FANET performance, the specifics of these two factors vary greatly depending on the needs of each application. As a result, different application-oriented trajectory optimizations have been proposed in the existing literature, like the models in [107; 108; 109; 110; 111; 112].  
All in all, efficient role management and trajectory optimization of UAVs is a real-time task which requires the ability to adapt to network changes and act accordingly. This demands for more application-oriented standardization and scalabel path planning protocols.