Smart City Gnosys
Smart city article details
| Title | Ddl: Empowering Delivery Drones With Large-Scale Urban Sensing Capability |
|---|---|
| ID_Doc | 17570 |
| Authors | Chen X.; Wang H.; Cheng Y.; Fu H.; Liu Y.; Dang F.; Liu Y.; Cui J.; Chen X. |
| Year | 2024 |
| Published | IEEE Journal on Selected Topics in Signal Processing, 18, 3 |
| DOI | http://dx.doi.org/10.1109/JSTSP.2024.3427371 |
| Abstract | Delivery drones provide a promising sensing platform for smart cities thanks to their city-wide infrastructure and large-scale deployment. However, due to limited battery lifetime and available resources, it is challenging to schedule delivery drones to derive both high sensing and delivery performance, which is a highly complicated optimization problem with several coupled decision variables. Meanwhile, this complex optimization problem involves multiple interconnected decision variables, making it even more complex. In this paper, we first propose a delivery drone-based sensing system and formulate a mixed-integer non-linear programming problem (MINLP) that jointly optimizes the sensing utility and delivery time, considering practical factors including energy capacity and available delivery drones. Then we provide an efficient solution that integrates the strength of deep reinforcement learning (DRL) and heuristic, which decouples the highly complicated optimization search process and replaces the heavy computation with a rapid approximation. Evaluation results compared with the state-of-the-art baselines show that DDL improves the scheduling quality by at least 46% on average. More importantly, our proposed method could effectively improve the computational efficiency, which is up to 98 times higher than the best baseline. © 2007-2012 IEEE. |
| Author Keywords | Cyber-physical systems; deep reinforcement learning; drone swarm; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
40784  | 0.868 | Shahzaad B.; Alkouz B.; Janszen J.; Bouguettaya A. | Optimizing Drone Delivery In Smart Cities | IEEE Internet Computing, 27, 4 (2023) |
| 11445 | 0.854 | Yun W.J.; Ha Y.J.; Jung S.; Kim J. | Autonomous Aerial Mobility Learning For Drone-Taxi Flight Control | International Conference on ICT Convergence, 2021-October (2021) |
| 59845 | 0.853 | Rinaldi M.; Primatesta S.; Bugaj M.; Rostáš J.; Guglieri G. | Urban Air Logistics With Unmanned Aerial Vehicles (Uavs): Double-Chromosome Genetic Task Scheduling With Safe Route Planning | Smart Cities, 7, 5 (2024) |
| 12858 | 0.853 | Bassolillo S.R.; D’Amato E.; Notaro I.; D’Agati L.; Merlino G.; Tricomi G. | Bridging Aco-Based Drone Logistics And Computing Continuum For Enhanced Smart City Applications | Drones, 9, 5 (2025) |