Joint Mission Assignment and Topology Management in the MissionCritical FANET In recent years, the emergence of flying ad hoc networks (FANETs) with multiple unmanned aerial vehicles (UAVs) can make it possible to effectively perform not only faroff missions but also assorted complex missions. In this paper, we consider a missioncritical FANET to perform given missions using multiple UAVs, taking into account a dynamic environment with a timevarying network topology. To effectively operate the missioncritical FANET, we study joint mission assignment and topology management problem aiming at maximizing the weighted sum of mission and network performances, while guaranteeing endtoend communications between missionperforming UAVs and their corresponding ground control stations (GCSs), interUAV safety distance maintenance, and other missionrelated constraints. To address this problem, we first develop three algorithms: One is to construct a missioncritical FANET from scratch, and the others are to manage the network topology and to switch UAV roles between missionperforming and datarelaying in response to the changes in the network topology. Then, we develop a dynamic missioncritical FANET operation algorithm incorporating the three algorithms with a few rules, by which the missioncritical FANET can be effectively managed and operated with reasonable computational complexity in the dynamic environment. Through simulation results, we show that our proposed algorithm works well in the dynamic environment while satisfying the constraints, and that its performance is not only superior to the existing algorithms, but also close to the optimal performance.

Learning Sensor Placement from Demonstration for UAV networks This work demonstrates how to leverage previous network expert demonstrations of UAV deployment to automate the drones placement in civil applications. Optimal UAV placement is an NPcomplete problem: it requires a closedform utility function that defines the environment and the UAV constraints, it is not unique and must be defined for each new UAV mission. This complex and timeconsuming process hinders the development of UAVnetworks in civil applications. We propose a method that leverages previous network expert solutions of UAVnetwork deployment to learn the expertxe2x80x99s untold utility function form demonstrations only. This is especially interesting as it may be difficult for the inspection expert to explicit his expertise into such a function as it is too complex. Once learned, our model generates a utility function which maxima match expert UAV locations. We test this method on a WiFi UAV network application inside a crowd simulator and reach similar qualityofservice as the expert. We show that our method is not limited to this UAV application and can be extended to other missions such as building monitoring.