Sharing instant delivery UAVs for crowdsensing: A data-driven performance study

Abstract

In recent years, there has been a significant increase in demand for instant deliveries, such as rapid delivery of takeaway food and medicine. Many logistics companies are planning to realize real-time delivery services through unmanned aerial vehicles (UAVs). However, costs of running such an autonomous delivery system are too expensive. Fortunately, urban management departments (UMD) that are responsible for monitoring urban environments have the intentions to cooperate with external companies and design crowdsensing methods, so that the duty can be ensured in an autonomous and low-cost method. Motivated by this, instant delivery UAVs are introduced into crowdsensing, so that UAVs can earn additional money for logistics companies while ensuring priority completion of instant deliveries, as well as provide environmental monitoring data to UMD. To be specific, some challenges are firstly identified to combine these two tasks based on real-world data in China. Then a set of data-driven UAVs sharing algorithms is proposed to achieve sensing for urban POIs (Points of Interest) and urban anomaly events. The results of simulations based on real-world datasets demonstrate the efficiency of our methods. The average sensing interval of the POIs within a 368.64π km2 area can be only ≈2.6 min. more than 81% of stochastic events are successfully predicted, over 84% (401 out of 476) of events are successfully sensed, and 60% of events have a UAV to arrive within 6.65min of their occurrence.