LAUSR

Despite the severity of the second wave of the novel coronavirus disease (COVID-19) and the recent hope for vaccine roll-outs, many public and private institutions are forced to resume their activities subject to ensuring an adequate sterilization of their premises. The existing off-the-shelf drones for such environment sanitization have limited flight-time and payload-carrying capacity. In this paper, we address this challenge by formulating an optimization problem to minimize the energy consumed by drones equipped with ultraviolet-C band (UV-C) panels. To solve this computationally hard problem, we propose several heuristics, such as a randomized path selection algorithm whose solution is further improved with a genetic algorithm-based UV-C drone-based sterilization (UV-CDS) scheduling technique. We consider educational institutions, confronting increasing infections, as an important use-case for the problem. Due to the energy constraint of the drones, the number of drones required for sterilization of the campus is smartly altered for various campus scenarios. The respective energy-efficient paths in the proposed heuristics and our envisioned UV-CDS are estimated for the drones. The performance is evaluated through extensive computer-based simulations which clearly demonstrates the effectiveness of UV-CDS in terms of sub-optimal performance and much faster execution time in contrast with the other methods.