UAVs hand off data to buses for cheaper and faster analytics
Adaptive and Cost-Efficient Joint Scheduling of UAV Routes and Analytics with Transit-Borne Fog
Distributed, Parallel, and Cluster Computing
Summary
Collecting data by drones in rural areas is hard because there are few cell towers to send it to in time. The authors suggest using public buses as moving helpers that collect data from drones at bus stops and carry it until they reach areas with cell coverage. They created a method to plan drone routes and decide whether to process data on the drone, stationary computers nearby, or on buses to meet deadlines and save energy and costs. Their method works better than others on real rural data and stays effective even when bus schedules change.
What this means in practice
- •For smart city planners: Schedule drones with transit routes to collect and relay data efficiently in rural or low-coverage areas for environmental or infrastructure monitoring.
- •For logistics managers: Coordinate delivery drones and public buses to optimize data transfer and processing while minimizing energy and operational costs during transit.
Authors
Suman Raj, Arindam Khanda, Gagana M D, Yogesh Simmhan, Sajal K. Das
Abstract
Unmanned Aerial Vehicles (UAVs) performing deadline-bound analytics over large rural areas cannot reliably offload workloads to sparse cellular base stations. We propose an approach that uses scheduled public buses as \textit{mobile fogs}: a UAV hands off data to a bus during a halt, and the bus carries it until its route enters cellular coverage. Because a public bus follows a fixed route and timetable, a handover depends on \textit{where} and \textit{when} the bus next reaches a cellular zone, rather than how near the stop is. We formulate a Mission Scheduling Problem over this model, co-scheduling UAV routes with the placement of each analytics task on the UAV edge, a stationary fog, or a bus, under deadline, energy, and cost constraints. Our \textit{Divide and Assign} (DA) heuristic selects the cheapest halt that still meets a task's deadline. Across 36 workload configurations in a rural region, derived from real cellular and transit data, DA achieves up to 20\% higher utility than the strongest heuristic and up to 41\% higher utility than the strongest adapted-prior scheduler, while incurring the lowest aggregate cost. The transit tier handles up to 71\% of drop-offs, raises task completion to 100\%, and reduces recharging cycles by up to 31\%. Finally, in the presence of traffic variability, the adaptive variant recovers 93\% of the utility the delays cost and maintains completion rates above 97\%.