论文标题
在移动边缘计算网络中用于分布式高架激光雷达的移动边缘计算网络中的延迟感知任务
A Latency-Aware Task Offloading in Mobile Edge Computing Network for Distributed Elevated LiDAR
论文作者
论文摘要
最近，由于能够降低AVS的成本和计算要求，减少映射区域的重叠传感器的数量，并允许使用相同的共享LIDAR LIDAR图数据的多个LIDAR传感应用程序，因此提出了高高的LIDAR（ELID）作为自动驾驶汽车（AV）当地LIDAR传感器（AV）的替代方案。由于ELID已从车辆中删除，因此必须在云中或边缘对其数据进行处理，因此需要一个优化的回程系统，该系统有效地分配数据以计算服务器。在本文中，我们通过制定混合智能编程问题来解决对优化的回程系统的需求，该问题最大程度地减少了上行链路链路和下行链路的平均延迟逐行传输和每个ELID的计算时间，同时考虑不同的带宽分配方案。我们表明，我们的模型能够为不同的拓扑分配资源，并且我们进行了灵敏度分析，以证明在不同情况下我们问题表达的稳健性。
Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since ELiDs have been removed from the vehicle, their data must be processed externally in the cloud or on the edge, necessitating an optimized backhaul system that allocates data efficiently to compute servers. In this paper, we address this need for an optimized backhaul system by formulating a mixed-integer programming problem that minimizes the average latency of the uplink and downlink hop-by-hop transmission plus computation time for each ELiD while considering different bandwidth allocation schemes. We show that our model is capable of allocating resources for differing topologies, and we perform a sensitivity analysis that demonstrates the robustness of our problem formulation under different circumstances.
