# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 5 UAV Data Relaying  
During disaster, using an additional way of communication to relay data between rescue teams and different zones in the disaster area would ease and speed up the rescue operations. Thus, UAVs have gained increasing attention as they can move easily from one place to another and they can communicate and relay data with other devices. However, UAVs are characterized by their high cost and limited battery lifetime. Therefore, it is of great importance to efficiently use UAVs such to optimize their paths when relaying data.  
Data collection as well as reporting to rescue teams leverages drones equipped with femto-cells as an on-demand communication infrastructure [8; 16]. To fully cover the area affected by the disaster and effectively provide wireless communication capability, the drone must visit all the nodes which have switched on the upmost tiers interface (i.e., nodes in the \(n_{U}\) tier). The solution proposed in this work operates in two phases.  
* _Search._ A drone flies over the area affected by the disaster so as to discover nodes and store their location. The drone follows an \(S\)-shaped route, whose curvature guarantees that all nodes can be discovered (see Figure 5).
* _Anchor points derivation and path planning._ Once the nodes are discovered, anchor points are then derived. Anchor points can be either \(n_{U}\) tier nodes or locations from which a drone can reach multiple \(n_{U}\) tier nodes, if possible. That is, an anchor point can be anywhere in between the \(n_{U}\) tier nodes it serves (Figure 6). Hence, there is no need for the drone to hover above each \(n_{3}\) tier node--hovering above the (fewer) anchor points suffices to serve all \(n_{U}\) tier nodes. Consequently, given such anchor points as an input to a path planning algorithm, the shortest path that visits all these points is then constructed. The drone then follows such a path and collects data (Figure 7).  
Figure 4: Smartphones clock drift.  
Different schemes to plan the drone's path that dictate the order of visit of the points are considered. Such schemes aim at finding the shortest path that visits all such points, while the drone must return to its initial location (to recharge, for instance). Such a problem, in fact, corresponds to the well-known NP-hard TSP problem. Other versions such as TSPN (TSP with Neighborhoods), CETSP (Close-Enough TSP), Covering Tour Problem, and Generalized TSP are extensively studied in literature [23; 24].  
The design of the path planning algorithms focuses especially on the energy consumption of the drone. That is, such algorithms aim at reducing the tour length of an UAV, hence the time it takes to fly  
Figure 5: During the search phase a drone flies over the area affected by a disaster and stores the location of the discovered nodes in the highest (i.e., \(n_{3}\)) tier of the network.  
Figure 6: Drone anchor points shown with stars.  
Figure 7: Shortest path for a drone to visit all the anchor points.  
over a disaster area and collect the data from the nodes. In fact, such reduces the energy expenditure of the UAV. More specifically, leveraging the cooperative communication and data relay protocol among the nodes underlaying the UAV yields to reducing the number of points a drone should visit. In fact, identifying anchor points from which a drone can serve more than one node results in a lower number of stops for the drone. Feeding such anchor points i to the existing TSP and CETSP algorithms [23; 24] comes down to construct the shortest path for a drone to follow, as shown in Figure 7.