A Latency-Aware Task Offloading in Mobile Edge Computing Network for Distributed Elevated LiDAR

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.

I. Introduction

Light detection and ranging (LiDAR) technology has taken root as a core sensing technology in various Intelligent Transportation Systems (ITS) applications, including, but not limited to, autonomous vehicles (AV) [1] and topological urban 3D mapping [2]. In AVs, LiDAR point cloud maps are combined with camera-based images and Radio Detection and Ranging (RADAR) detection data for the purpose of Simultaneous Locating and Mapping (SLAM) of AVs [3], [4], which is used to aid the AV control systems in navigating through dynamic surroundings in real-time [5].