Age of Processing-Based Data Offloading for Autonomous Vehicles in Multi-RATs Open RAN

TL;DR

This paper introduces an age of processing-based data offloading method for autonomous vehicles, leveraging machine learning, multi-RATs, and edge computing to reduce latency and improve safety.

Contribution

It proposes a novel age-aware offloading framework using optimization and dual decomposition, considering multi-RATs and edge clouds for autonomous vehicle data processing.

Findings

Achieves 90.34% reduction in age of processing compared to existing methods.

Effectively preselects RATs to optimize offloading delay.

Enhances safety and reliability in autonomous vehicle data management.

Abstract

Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle's cost, quickly drains its battery, and exhausts its computing resources. Therefore, offloading tasks onto the cloud is required. Still, data offloading is challenging due to low latency requirements for safe and reliable vehicle driving decisions. Moreover, age of processing was not considered in prior research dealing with low-latency offloading for autonomous vehicles. This paper proposes an age of processing-based offloading approach for autonomous vehicles using unsupervised machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge Computing in Open Radio Access Network (O-RAN). We design a collaboration space of edge clouds to process data in proximity to autonomous vehicles. To reduce the variation in offloading delay, we propose a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local resources are insufficient. We formulate an optimization problem for age-based offloading that minimizes elapsed time from generating tasks and receiving computation output. To handle this non-convex problem, we develop a surrogate problem. Then, we use the Lagrangian method to transform the surrogate problem to unconstrained optimization problem and apply the dual decomposition method. The simulation results show that our approach significantly minimizes the age of processing in data offloading with 90.34 % improvement over similar method.