# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 2 Mobile Ad Hoc Networks (MANETs)
### Scope of the Paper  
MANETs have the potential to transform wireless communication due to their flexibility and self-organizing capabilities [34; 35; 36]. Despite this potential, they face significant challenges [4; 5; 7; 12; 14; 15; 16; 29] that can hinder their optimal operation. Understanding these challenges is crucial to improving their robustness, security, and efficiency.  
One of the most prominent challenges in MANETs is the dynamic nature of the network topology [14; 15]. Given the mobility of nodes, the network topology can change over time. This can cause path breakages and result in intermittent connectivity, complicating the process of routing data between nodes. Designing adaptive routing protocols [17; 30] that can quickly react to such changes is a non-trivial task, requiring advanced algorithms and efficient network management strategies [12].  
As the number of nodes in the network increases, the overhead involved in managing the network, maintaining routing tables, and ensuring seamless communication can become increasingly complex. Scalability issues [15] can result in decreased network performance, increased latency, and reduced reliability.  
We propose the use of drone-borne access points to form MANETs. Our investigations are kept generic and within the broad range of MANETs designed to enable client-to-root connectivity, see Section 2.3. We focus on the management of the dynamic network topology (i.e., the self-organizing [37] movement of the nodes) and do so in a scalable manner. We are restricting ourselves to MANETs which are primarily concerned with connecting clients through a network with a root or data sink [13].  
Topology management, i.e., the positioning and connecting of nodes in a MANET, is understood to be one of the most important challenges. Several types/classes of approaches have been proposed by the community in the literature. We distinguish by the way they control the swarm: centralized [38; 39; 40], decentralized [38; 41] or hybrid, and the mechanism used to determine the individual node movement: location- [42], virtual force- [43] or machine learning-based [44; 45; 46]. The approach presented in Section 4 is decentralized: the drones in the MANET base their movement and connectivity decisions entirely on local information. However, since the base station (the root in the network topology) can trigger the release of additional drones into the swarm, there is a central component in the system, and one could argue that our approach therefore constitutes a hybrid mechanism.  
The presented approach is force-based (with the three forces discussed in Sections 4.1.1-4.1.3). The nodes are assumed to be fully aware of their position (see Section 3.3) but this is merely a simplification that allows each drone to compute the exact distance to, and the direction of, another drone. These values could also be inferred through signal analysis. In general, relative position is used in the calculation of all virtual forces (e.g., repulsive between nearby drones) which does imply the reciprocal knowledge of neighboring drones' locations. However, we do not concern ourselves with how this information is procured (e.g., using visual signals).