Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This article considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile edge computing (MEC), including unmanned aerial vehicles (UAVs), and the fixed-position MEC servers, to optimize the system cost including execution time, energy consumption, and the ID rental price. Moreover, to increase the access probability of IV by the UAVs, the geographical area is divided into small partitions and schedule the UAVs regarding the regional IV density dynamically. A minimum incremental task allocation algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time. Experimental results show the proposed solution can significantly reduce the system cost.

中文翻译：

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用于工业移动边缘计算的车辆计算卸载


由于本地计算资源有限，工业车辆计算一旦协同感知和收集数据，就需要将时延敏感且复杂需求的计算任务卸载给其他智能设备（ID）。本文考虑将工业车辆（IV）的部分计算任务卸载到工业移动边缘计算（MEC）的多个可用ID，包括无人机（UAV）和固定位置MEC服务器，以优化系统成本，包括执行时间、能耗和 ID 租赁价格。此外，为了提高无人机对IV的访问概率，将地理区域划分为小分区，并根据区域IV密度动态调度无人机。提出一种最小增量任务分配算法，对整个任务进行划分，并以每次任务增量最小的方式分配划分的单元。实验结果表明，所提出的解决方案可以显着降低系统成本。