# LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways
## 2 Related Work  
Establishing a relay wireless ad hoc network of UAVs comes with a set of non trivial problems: how to optimally place the UAVs in a 3D environment, and how to plan the movement of UAVs to avoid collisions while reaching the target objective? A problem that frequently arises is the connectivity versus coverage problem. In an ideal situation, the mesh network should provide the maximum possible coverage, while at the same time it should maintain the connectivity between its members. Unfortunately, due to the limited available resources (i.e., number of UAVs), a compromise between both requirements is typically needed. The algorithms that have been developed to tackle the aforementioned problems fall in two main categories: centralized and distributed. Centralized algorithms rely on one single entity having full knowledge and control of the nodes forming the network. This approach typically produces close to optimal results when the location information is timely and reliable, but suffers from the single point of failure problem and, if the dimension of the problem is big enough, location information dissemination and the amount of computations can generate substantial delays. Mixed integer programming [5], evolutionary algorithms [6; 7] or potential fields [8] are typically used. On the other hand, distributed algorithms provide less optimal solutions, but computations are typically simpler, based on local information distributed among the nodes, thus making the network more responsive and resilient in case of unexpected changes. Most of the existing distributed algorithms adapt concepts coming from physics or natural animal behavior. Such is the case of VSF approaches like the one presented in this paper. Consequently, this section will focus on distributed approaches proposed so far. [PERSON] et al. [9] propose a flocking based system, where UAVs self-organize to follow military units deployed and moving on the ground. The main purpose is to minimize the number of allocated UAVs in comparison with a full area coverage scheme. Fixed wing UAVs are assumed, with a constraint being considered on the maximum turn angle. A state machine with four states is proposed, which runs independently in each UAV, leading to an emergent flocking behavior. The state machine defines when the UAV should be repelled or attracted by its neighbors depending on mutual distance, when to approach the centroid of ground nodes within range, or when to randomly walk to find positions where more ground nodes are covered. The results show that the UAVs are able to follow and effectively cover the ground nodes when they move, though performance is clearly better when ground node movement is local and takes place within a constrained area. [PERSON] et al. [10] consider a distributed decision approach to maintain a coherent mesh network of UAVs with the objective of exploring a 3D area. The swarm has also the additional requirement of keeping the connectivity to a ground base station. Two scenarios are studied by the authors: one in which the connectivity to the base station is permanent (Bounded Relaying) and one in which some disconnections are allowed for the purpose of extending the exploration (Release and Return). Each UAV in the network has the ability of self-selecting a different role, depending on the topology of the network: Scout Agents (SA) are assigned the task of exploration and sensing, Relay Nodes (RN) keep the communication between one or more UAVs and the base station, Articulation Points (AP) link two clusters that otherwise would be fragmented and Returnees (R) are drones that, in a Release and Return scenario, return to regain connectivity after being detached from the network. In order to maintain mesh connectivity, the authors propose a communication-aware algorithm based on virtual potential fields called Communication Aware Potential Fields (CAPF).