# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 5 Performance Evaluation
### Results  
Simulation results confirm the distributed algorithm's ability to grow a multi-hop access network capable of adapting its topography (localization of nodes) and topology (connections between them) to service a population of clients featuring an arbitrary and heterogeneous distribution, see Figure 2.  
Furthermore, they also indicate that the parallel execution of the decision function (which governs individual placement) by all swarm members produces a stable or near stable network configuration while clients' distribution remains static (i.e., successful self-organization). Most participating drones end up \"oscillating\" around a fixed location and keeping the same role/level in the tree topology for long periods.  
Because the algorithm can produce a suitable fixed distribution of access points while the clients remain immobile, it can be left to run in the background even when the network is fully grown and LQM is satisfactory across the entire target population. Consequently, when the clients do relocate (see Section 5.1 for details on how this was implemented), no specific procedure needs to be invoked for the swarm to reconfigure. Through a combination of individual movement, the retirement of units made idle by the change in the demand profile, reorganization of the topology (_parent-child_ relationships), and dispatching of new drones toward points of heightened stress (all procedures used to grow the initial network), the swarm can spontaneously adapt to any new situation.  
Selected results are presented to illustrate system behavior. Figure 2 show the evolution of the chosen LQM approximation over time. This variable is defined as the minimum signal strength when considering the entire sequence of hops between the root and the client (\"weakest-link\" hypothesis) relative to the maximum achievable (signal strength plateaus at 1 for a distance \(r\) equal or lower than one space unit, i.e., \(\min(1,\frac{1}{r^{2}})\).  
As expected, at relocation time, both average and lowest LQM drop sharply, whereas variability (sdev) rises in proportion. After a few steps though, the system recovers as the swarm reorganizes itself to accommodate the new client distribution.  
Figure 3 shows the evolution of the number of drones involved in providing the service over time. Unsurprisingly, it follows the same pattern as LQM during the network growth phase (\(t=0\to t=400\) or \(t=0\to t=800\)) and after subsequent clients' relocation.  
Two aspects may be worth emphasizing:  
1. Swarm size appears to saturate at around 80 for the used values (coverage area, number of clients/clusters), which is lower than the total number of drones available (100), indicating the system's ability to commit only the necessary resources.
2. After relocation, the total number of drones in the air does not drop below about 60, so the adaptation process is not equivalent to a \"reboot\". Only about 25% of the swarm (\(\approx\)20 units) is being \"retired\" (then progressively replaced), whereas the remaining 75% just \"follow the clients\" in their migration.  
Figure 3: Evolution of swarm size (simultaneously airborne drones). Reported are the averages over 100 independent simulation runs. NB: all parameters, including the total number of clients and drone fleet size, are identical in both scenarios. Only the frequency of relocation (the interval between the mobile clients moving to another location) varies (800 vs. 400 time units interval).