# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 6 Conclusions and Future Work
### Future Work  
The work presented is based on some significant simplifications and constitutes a theoretical investigation. Regarding the continuation of our work, we are already considering several directions:  
* We have not explored extreme outlier scenarios, such as the case where there are no clients present or where all clients have aggregated at the extreme ends of the map. * The introduction mentions resilience as a benefit for MANET's (see Section 1.2). The MANET approach we developed does not provide any redundancy. However, we expect that this can be designed into the swarm and are hoping to proceed with work focusing on this (and the formal verification thereof) in the near future. * We argue that the parameters needed to control energy efficiency are already included in the force-based control of the nodes in the network: in Section 4.1.3, Equation (7), we set the inter-drone distance for which the value of this force becomes infinitesimally small to 1. As distance affects energy efficiency later work can use this part of the model to modulate the power required for inter-drone communication. Similarly, the LQM (Section 3.2) is set to plateau for the distance value of 1. Despite this connection to the energy cost incurred when transmitting data over the resulting MANET, our current investigations do not assess this cost. Future work, especially work directed at specific applications, will include considerations for this network performance indicator. * During performance evaluation we noted that small changes in the setup as well as in Equations (5)-(7) led to peculiar emergent _behaviors_ of the swarm (such as, e.g., a long \(arm\) of connected drones swaying from left to right until they connect with a client). Future work will investigate the design and use of such behaviors and the environmental conditions necessary to achieve them. * In Equation (2) we defined the LQM value as simply the weakest link in the route from root to client. We can define this value in a more complex manner to reflect additional properties of the connection, the resulting LQM will be subject to the signal deterioration in the entire route to the root. The recursive definition for \(LQM_{i}\) for any node \(i\) connected to the best available access point \(j\) is thus: \[LQM_{i}=\frac{LQM_{j}}{\max(1,r_{ij})^{2}}\] Under this revised definition, \(any\) sub-optimal link anywhere in the connection to the router will further degrade the overall LQM value (the presented work only considers the last link). This affects the deployment of additional nodes in at least two ways. Firstly, since sub-optimal links closer to the router may further lower the LQM value this directly impacts the deployment of additional nodes, which may now be deployed earlier (thereby potentially improving the individual link quality and thus the overall connectivity). Secondly, the LQM values now reflect the actual connection quality better and are thus more likely to result in new nodes being dispatched to the locations where they are needed most. To motivate the latter claim, consider two routes of LQM value 0.6, one where all links are perfect except one and another where all links have the sub-optimal value of 0.6. The second route would (correctly) receive a much lower overall LQM value and thus be allocated an additional drone first. Exploring the impact of this revised performance measure is future work. * In Section 2.3 we have specifically restricted our scenario to the 2D domain. With a large number of projects in our labs being rooted in the maritime sector, the presented work currently meets our needs. That being said, while we do not foresee any issues with extending the theory to 3D in general, working on an amended approach for obstacle-rich environments or challenging landscapes (such as, e.g., urban canyons or indoor search-and-rescue) is considered future work. Conceptualization, FS. ; methodology, FS. ; software, FS. ; validation, FS. and H.H. ; formal analysis, F.S., H.H. and A.A.; investigation, F.S., H.H. and A.A.; resources, F.S.