Computation Pre-Offloading for MEC-Enabled Vehicular Networks via Trajectory Prediction

TL;DR

This paper introduces a trajectory prediction-based pre-offloading algorithm for vehicular networks that reduces task delay and improves resource utilization by forecasting vehicle movements and planning resource allocation in advance.

Contribution

The paper proposes a novel combination of LSTM-based trajectory prediction and DDQN-based resource allocation for MEC-enabled vehicular networks, addressing latency issues in task offloading.

Findings

Significantly reduces task processing delay compared to traditional methods.

Improves computational resource utilization in vehicular networks.

Effective prediction of vehicle trajectories enhances offloading decisions.

Abstract

Task offloading is of paramount importance to efficiently orchestrate vehicular wireless networks, necessitating the availability of information regarding the current network status and computational resources. However, due to the mobility of the vehicles and the limited computational resources for performing task offloading in near-real-time, such schemes may require high latency, thus, become even infeasible. To address this issue, in this paper, we present a Trajectory Prediction-based Pre-offloading Decision (TPPD) algorithm for analyzing the historical trajectories of vehicles to predict their future coordinates, thereby allowing for computational resource allocation in advance. We first utilize the Long Short-Term Memory (LSTM) network model to predict each vehicle's movement trajectory. Then, based on the task requirements and the predicted trajectories, we devise a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) that enables the edge server to minimize task processing delay, while ensuring effective utilization of the available computational resources. Our simulation results verify the effectiveness of the proposed approach, showcasing that, as compared with traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.