DeliverSense: Efficient Delivery Drone Scheduling for Crowdsensing with Deep Reinforcement Learning


Delivery drones provide a promising sensing platform for Mobile Crowdsensing (MCS) due to their high mobility and large-scale deployment. However, due to limited battery lifetime and available resources, it is challenging to schedule large-scale delivery drones to derive both high crowdsensing and delivery performance, which is a highly complicated optimization problem with several coupled decision variables. In this paper, we first formalize the delivery drones scheduling problem as a mixed-integer nonlinear programming problem with both sensing and delivery utilities as dual objectives. Then we propose a novel framework DeliverSense with a reinforcement learning-based efficient solution, which decouples the highly complicated optimization search process and replaces the heavy computation via fast approximation. Evaluation results compared with state-of-the-art baseline show that DeliverSense improves the total utility by 13% and 23% on average under various energy budgets and numbers of selected routes, respectively. More importantly, our proposed method achieves much lower computational complexity which is nearly 3 times lower than the baseline.

In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Research Assistant Professor

My research interests include AIoT, edge computing, and mobile security.