|Angelo Trotta||University of Bologna, Italy|
|Fabio Dandreagiovanni||CNRS, Sorbonne University - UTC, France|
|Marco Di Felice||University of Bologna, Italy|
|Enrico Natalizio||Université de Technologie de Compiègne, France|
|Kaushik Chowdhury||Northeastern University, USA|
This paper proposes a network architecture and supporting optimization framework that allows Unmanned Aerial Vehicles (UAVs) to perform city-scale video monitoring of a set of Points of Interest (PoI). Our approach is systems-driven, relying on experimental studies to identify the permissible number of hops for multi-UAV video relaying in a noisy 3-D environment. Our architecture itself is innovative in the sense that it defines a mathematical framework for selecting the UAVs for periodic recharging by landing on public transportation buses, and then 'riding' the bus to the successive chosen PoI. Specifically, we show that our UAV scheduler can be modeled as an instance of multi-commodity flow problems, and mathematically solved through Mixed Integer Linear Programming (MILP) techniques. Thus, our centralized formulation identifies the UAV, the next bus, and the next PoI, given the information about energy thresholds, the bus routes in the city and their next arrival times, to ensure persistent and reliable video coverage of all PoIs in the city. Finally, our work is validated via emulation of a city environment with live traffic updates from a real bus transportation network.