  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.
