Edge-Assisted Indexing for Highly Dynamic and Static Data in Mixed Reality Connected Autonomous Vehicles

Abstract

The integration of Mixed Reality (MR) technology into Autonomous Vehicles (AVs) has ushered in a new era for the automotive industry, offering heightened safety, convenience, and passenger comfort. However, the substantial and varied data generated by MR-Connected AVs (MR-CAVs), encompassing both highly dynamic and static information, presents formidable challenges for efficient data management and retrieval. In this paper, we formulate our indexing problem as a constrained optimization problem, with the aim of maximizing the utility function that represents the overall performance of our indexing system. This optimization problem encompasses multiple decision variables and constraints, rendering it mathematically infeasible to solve directly. Therefore, we propose a heuristic algorithm to address the combinatorial complexity of the problem. Our heuristic indexing algorithm efficiently divides data into highly dynamic and static categories, distributing the index across Roadside Units (RSUs) and optimizing query processing. Our approach takes advantage of the computational capabilities of edge servers or RSUs to perform indexing operations, thereby shifting the burden away from the vehicles themselves. Our algorithm strategically places data in the cache, optimizing cache hit rate and space utilization while reducing latency. The quantitative evaluation demonstrates the superiority of our proposed scheme, with significant reductions in latency (averaging 27%–49.25%), a 30.75% improvement in throughput, a 22.50% enhancement in cache hit rate, and a 32%–50.75% improvement in space utilization compared to baseline schemes.