# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 6 Conclusions  
In this paper, a new system called LoRaUAV has been proposed. The objective of the system is to extend the coverage offered by a BS to a set of mobile GNs through UAV relay GWs. This is achieved thanks to a network that consists of a LoRaWAN segment for GNs-GW communications and a WiFi ad hoc network for GWs-BS communications. The core of the system is the distributed LoRaUAV mobility algorithm based on VSFs, designed to adapt the UAV swarm to the changing topology of GNs. The basic VSF mobility is expanded by the CRM and MP algorithms, designed, respectively, to increase the GN coverage through a better UAV distribution and to restore the connection of isolated GNs through movement prediction. The developed algorithms were evaluated through a ns-3 custom-built \(lorawsf\) module. Simulations were run on a model of a wildfire disaster scenario. The performance of each algorithm has been assessed through relevant QoS metrics: the average end-to-end PRR (AE-PRR) and the average delay (ATD). The LoRaUAV VSF algorithm has also been compared with another  
Figure 7: Frequency and duration of disconnections for DF VSF (**A**), LoRaUAV VSF (**B**), LoRaUAV VSF+CRM (**C**) and LoRaUAV VSF+CRM+MP (**D**).  
similar VSF algorithm proposed in the literature. It has been shown that basic LoRaUAV VSF achieves a small average AE-PRR improvement that affects only some values of \(K_{p}\), with more significant gains when 4 or 5 teams are deployed. The most significant AE-PRR improvements are obtained thanks to the CRM algorithm. In fact, the AE-PRR shows a double-digit increment in most of the tested configurations. The ATD is the metric that is more negatively affected, but long delays only affect a small percentage of packets. The synergy between the CRM and MP algorithms produces another AE-PRR improvement, particularly significant when teams split. The developed extensions to the basic VSF algorithm were also demonstrated to effectively help in reducing almost by half the isolation time of GNs.  
Future evolutions of this work will consider the impact of LoRaUAV dynamics on the Adaptive Data Rate mechanism of LoRaWAN, dynamic \(K_{p}\) optimization, more realistic UAV models, more sophisticated prediction algorithms (e.g., Kalman-based tracking), integration with centralized meta-heuristic optimization algorithms (e.g., [6]), downlink messages and the existence of multiple BSs.  
Conceptualization, M.S., A.G.; methodology, M.S., S.S., A.G.; software, M.S.; validation, M.S., S.S., A.G.; formal analysis, M.S., A.G.; investigation, M.S.; resources, A.G.; data curation, S.S., A.G.; writing-original draft preparation, M.S., S.S., A.G.; writing-review and editing, M.S., S.S., A.G.; visualization, M.S., S.S., A.G.; supervision, S.S., A.G.; project administration, A.G.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by Portuguese national funds through Fundacao para a Ciencia e Tecnologia (FCT) with reference UIDB/50021/2020, by Fundacao Calouste Gulbenkian, and also by FITEC-Programa Interface, with reference CIT \"INOV-INESC Inovacao-Financiamento Base\".  
The authors declare no conflict of interest.