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Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urbanapplications like traffic monitoring and construction surveys. Autonomousnavigation allows drones to visit waypoints and accomplish activities as partof their mission. A common activity is to hover and observe a location usingon-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos tobe analyzed for automated decision making. UAVs also host edge computingcapability for on-board inferencing by such DNNs. To this end, for a fleet ofdrones, we propose a novel Mission Scheduling Problem (MSP) that co-schedulesthe flight routes to visit and record video at waypoints, and their subsequenton-board edge analytics. The proposed schedule maximizes the utility from theactivities while meeting activity deadlines as well as energy and computingconstraints. We first prove that MSP is NP-hard and then optimally solve it byformulating a mixed integer linear programming (MILP) problem. Next, we designtwo efficient heuristic algorithms, JSC and VRC, that provide fast sub-optimalsolutions. Evaluation of these three schedulers using real drone tracesdemonstrate utility-runtime trade-offs under diverse workloads.

Heuristic Algorithms for Co-scheduling of Edge Analytics and Routes for UAV Fleet Missions