Sensing the cities with social-aware unmanned aerial vehicles

The increasing diffusion of smart devices opens to a new era for collecting large quantities of data from urban areas. Sensing information can be collected by using existing network infrastructures, but also by adopting small, cheap and configurable aerial vehicles, namely drones. Our work focusses on studying how to optimize their adoption for smart city applications designed to gather sensing data from user's devices roaming on the ground. To this purpose, we used HUMsim, a tool which generates realistic human traces, to mimic pedestrian mobility. From this dataset, we extract some sociality features that we exploit to plan a social-aware drone trajectory with the goal of maximizing the opportunities of interaction between drone and devices. Our experiments compare social-aware and social-oblivious trajectories showing that knowing the way people move and interact boosts the amount of retrievable data.