Adaptive and Cost-Efficient Joint Scheduling of UAV Routes and Analytics with Transit-Borne Fog

2026-09-21

AI Summary

本文提出一种利用公共汽车作为移动雾计算节点的方法，以解决无人机在偏远地区执行任务时无法可靠卸载工作负载的问题，并通过联合调度无人机路线和分析任务实现成本效益。

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\%.

Problem

• UAVs
• deadline-bound analytics
• rural areas
• cellular base stations
• mobile fogs

Innovation

• UAV
• Mobile Fog
• Scheduling
• Adaptive Algorithm
• Cost-Efficiency