自适应且成本高效的无人机航线与基于公交搭载雾计算的分析任务联合调度
Adaptive and Cost-Efficient Joint Scheduling of UAV Routes and Analytics with Transit-Borne Fog
- University of Chicago（芝加哥大学）
- Kennesaw State University（肯尼索州立大学）
- Missouri University of Science & Technology（密苏里科技大学）
- Indian Institute of Science (IISc)（印度科学学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对农村无人机任务卸载难题，提出利用公交作为移动雾，联合调度航线与任务放置，DA启发式在36种配置下效用提升最高41%，成本最低。
AI中文摘要：
在广阔农村地区执行有截止期限约束的分析任务的无人机（UAV）无法可靠地将工作负载卸载到稀疏的蜂窝基站。我们提出了一种利用定时公共汽车作为“移动雾”的方法：无人机在公交车停靠时将数据交给公交车，公交车携带数据直至其路线进入蜂窝覆盖区域。由于公共汽车遵循固定的路线和时间表，交接取决于公交车下一次到达蜂窝区域的地点和时间，而非停靠站的远近。我们在此模型上形式化了一个任务调度问题，联合调度无人机航线与每个分析任务在无人机边缘、固定雾节点或公交车上的放置，同时满足截止期限、能量和成本约束。我们的“分而治之”（Divide and Assign, DA）启发式算法选择仍能满足任务截止期限的最便宜停靠点。在基于真实蜂窝和交通数据生成的农村地区36种工作负载配置中，DA相比最强启发式算法实现了高达20%的效用提升，相比最强的适配先验调度器实现了高达41%的效用提升，同时总成本最低。公交层处理了高达71%的下车任务，将任务完成率提升至100%，并将充电循环次数减少了高达31%。最后，在存在交通变化的情况下，自适应变体恢复了延迟造成效用的93%，并将完成率维持在97%以上。
英文摘要：
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\%.
