## 2 Related Works
Therefore, they investigate the adaptation of the forwarding decision process according to the nodes' neighborhood, by a proposal called Adaptive Probabilistic Epidemic Protocol (APEP). An aspect that is common with our work is the need to deliver messages from the ground sensors to the UAVs. As the positions of the UAVs are unknown, they try to address the problem by a flooding-based method, whose overhead is reduced by the probabilistic decision making process and by the knowledge about the neighborhood of a node on the ground. Our approach is different because it tries to provide information about the UAVs' movement to the ground nodes, so that the ground nodes can forward messages in the direction of the most suitable UAVs, thus reducing the drawbacks typical of flooding-based mechanisms. In [2], a divide and conquer solution for surveillance of large areas is proposed, in which static and mobile sensors provide coverage in an area. Static sensors are deployed in selected regions, such as the boundaries of the area, while mobile sensors move around these regions. The coverage problem is also studied in [20], in which the capability of area coverage by regularly and randomly deployed sensors is compared. The main difference to our approach is that we focus on the cooperation among different types of nodes, while they analyze the nodes' distribution in order to increase the area coverage. Focusing in this aspect, their approach has no commitment in reducing communication costs as presented in our proposal, even considering that specific nodes' arrangements can provide this feature as a side effect. Other related approaches [21; 22; 23; 24] handle the problem of mobile sinks in WSNs, which can be mapped to the scenario addressed in our work, considering the UAVs as mobile sinks. The goal of delivering sensed data to mobile sinks reported in these related works is comparable to our concept of alarm delivery. Some of these proposals consider that the mobile sinks may decide about their movement in order to facilitate the message delivery by static sensor nodes and thus optimize the energy usage in the network as a whole [21; 22]. Other proposals assume that the movement of the sinks is at least predictable, as in [23]. The assumptions about mobility presented in these works differ from ours, as there is no predictability in the movement pattern of the UAVs in our work. Moreover,the UAVs cannot present a preference to move towards the direction of a given group of nodes, before the occurrence of an alarm, as there is no indication that they would be needed in that location. Also considering mobile sinks, in [24] the concept of delay tolerant networks (DTN) is explored. Authors report experiments with the deployment of mobile nodes to collect information from static sensors to be used in agricultural applications. To drive the movement of data from the sensor nodes towards a central processing node via the mobile sink, authors use potential-based routing that sets higher potential to data providers, an intermediary potential to the mobile sinks and a lower one to the central node. Their potential-based routing is to some extent comparable to our pheromone-based one, but, while the pheromone-based strategy addresses the dynamicity imposed by the movement of the UAVs, their potential-based one is statically configured before runtime. In [25] the deployment of additional mobile nodes that are used as relay nodes to improve the network performance is proposed. According to the network lifetime dynamics, _i.e._, nodes' movement, interferences, among other events, these additional relay nodes change their placement according to the link quality, so that network connectivity is enhanced. This work explores an approach based on deployment of additional mobile nodes, and of controlling these nodes' movements to facilitate message delivery, combining the ideas presented in [17, 21] and [22], thus having the same comparison to our proposal as discussed above.