# A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs
## 2 Related Work
On the other hand, in [20], the authors proposed a relay placement mechanism in order to find the ideal location for UAVs that act as relay nodes, in order to support live video transmissions with satisfactory quality of experience to the users and improving the connectivity. The authors considered a flying ad hoc network scenario implemented on the Mobile MultiMedia Wireless Sensor Network (M3 WSN) OMNeT++ framework [21] composed of 30 UAVs moving over an area of 150 m \(\times\) 150 m. Although the results appear to be satisfactory in terms of maintaining network connectivity while supporting real time multimedia transmissions, the entire terrain scenario can be considered reasonably small. In [22], the authors considered the final deployment location of an UAV facility in a three-dimensional space with the aim of reaching a balance between service quality and interference. The authors took different scenarios into account, where either each user is selfish, and prefers the UAV to be located as close to himself as possible, or not, and they proposed a strategy-proof mechanism to overcome this issue. The success of the mechanism depends on whether the users report their locations truthfully. The authors presented empirical analysis in a single UAV scenario. In [23], the authors addressed the mission assignment and location management for UAVs in a mission-critical flying ad hoc network. The authors considered a scenario where the origin node is the ground control station and the destination node is a mission-performing UAV. Thus, the model aims to find UAVs' optimal mission assignment and locations together by maximizing the weighted sum of the communication performance and the mission performance functions. They assumed that UAVs that are not assigned to any mission are operated as relay nodes. The authors considered the communication performance as the lowest channel capacity among the links belonging to routing paths. In order to evaluate the communication performance, the authors did not consider area or coverage nor delay or data link rates, instead they considered a function in terms of the mission assignments, the locations of all nodes and the routing protocol in use. They proposed a Particle Swarm Optimization algorithm in order to solve the joint problem. A simulation scenario with 7 to 20 available UAVs and two missions was considered. In [24], the authors dealt with the UAV placement problem in a multi-channel UAV network, in order to be resilient to any device or link capability loss. The authors modeled the problem that was based on [PERSON]'s law, with users being represented as positive charges and drones as negative charges. In this model, UAVs are supposed to be attracted by users within their sensing range. By doing so, the authors focused on an initial UAV positioning in order to seek an optimal coverage. In the proposed model, the authors took into account the mean lifetime of the drones, the IEEE 802.11 communication protocol and the drones capacity. The proposal was evaluated with the help of a simulator developed by the authors, considering 500 users and up to 10 UAVs. The proposed framework was set up for particular events, such as a public gathering or a disaster situation where the network should be quickly deployed. However, the work failed to present numerical values regarding the time that is spent on finding a solution and the delay related to the data link rate. More recently, in [25], the authors investigated the router node placement problem within a specific geographical area. The idea was to form a viable, UAV-composed, ad hoc wireless mesh network that has performance measures optimized via a rapidly exploring random trees-based algorithm. The authors proposed a model that uses network coverage as an objective function which asses the total geographical area covered by the wireless mesh network.