The use of autonomous unmanned aerial vehicles (UAVs) or drones has emerged to efficiently collect data from mobile sensors when there is no infrastructure available. The drones can form a flying ad-hoc network through which the sensors can send their data to a base station at any time. In this paper, we present a mixed integer linear program to find the drones' optimal trajectories to form and maintain this network through time while minimizing their movements and energy consumption. Furthermore we analyze the trade-off between distance and energy, where increasing the drones' mobility can reduce their energy consumption, and derive a fair trade-off optimal solution to balance the two opposite objectives.

This paper addresses the data collection problem using the minimum number of unmanned aerial vehicles (UAVs) in a disaster management scenario where mobile sensors are investigating the devastated area. Critical information needs to be quickly gathered for processing by the rescue team, so the use of UAVs in this situation is of great interest. We propose an optimal model for computing the trajectories of the UAVs while guaranteeing the total coverage of the ground mobile sensors and connectivity among the UAVs with a central base station dedicated to data processing. Our model is based on a decomposition model and is solved effectively using column generation. We show that we can provide a plan for deploying the UAVs minimizing the total traveled distance.

The problem of the lifetime of connected objects, in most use cases (Industrial Internet of Things (IIoT), disaster management, etc.) is an essential element of the proposed solutions. Radio frequency (RF) harvesting of sensor batteries is an attractive solution, however, it does not scale up if it has to be done by human operators, and becomes impossible if the objects are located in unreachable places. An innovative solution consists of using fleets of drones to take care of this regular recharge. In this paper, we focus on the self-organised deployment of a fleet of drones to solve this problem, taking into account the multiple constraints involved. We propose a two-step optimization framework based on an optimal orchestration solution to reduce the recharging time of a complete sensor system, by optimizing the number of drones, the overall flight time and their energy consumption. We illustrate the performance of our framework that ensures the drones avoid conflicts to guarantee a higher energy harvesting efficiency (establishment of optimal drone positions and planning of the global flight plan).