Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that co-schedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the utility from the activities while meeting activity deadlines as well as energy and computing constraints. We first prove that MSP is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design two efficient heuristic algorithms, JSC and VRC, that provide fast sub-optimal solutions. Evaluation of these three schedulers using real drone traces demonstrate utility-runtime trade-offs under diverse workloads.
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Virginia Tech（弗吉尼亚理工大学）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；Ericsson Research（爱立信研究院）；Princeton University（普林斯顿大学）；University of Oulu（奥卢大学）
LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networks
Real-Time and Embedded Computing Systems Research Centre (CISTER)（实时与嵌入式计算系统研究中心（CISTER））；Faculdade de Engenharia da Universidade do Porto（葡萄牙波尔图大学工程学院）；School of Computer Science, McGill University（麦吉尔大学计算机科学学院）；Knight Foundation School of Computing and Information Sciences, Florida International University（国际大学计算与信息科学学院（Knight基金会））
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV
Central South University（中南大学）；Singapore Management University（新加坡管理大学）；University of Alberta（阿尔伯塔大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；Polish Academy of Sciences（波兰科学院）
Routing and Scheduling Optimization for Urban Air Mobility Fleet Management using Quantum Annealing
Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究科）；Sigma-i Co., Ltd.（Sigma-i 公司）；Sumitomo Corporation（住友商事）；Department of Physics, Tokyo Institute of Technology（东京工业大学物理系）
