A Dynamic-Pricing-Based Offloading and Resource Allocation Scheme With Data Security for Vehicle Platoon

With the accelerated growth of the Internet of Vehicles (IoV), secure and efficient task offloading of vehicle has emerged as a critical challenge, particularly in highway scenarios. Traditional mobile edge computing (MEC) solutions face significant limitations in these environments due to frequent link disruptions and the dynamic nature of vehicle movements. Platoon offloading is considered a feasible solution, to address these challenges, we propose a novel dynamic pricing-based task offloading and resource allocation scheme specifically tailored for vehicle platoons, integrating robust data security measures. Our scheme employs a Stackelberg game framework to model the interaction between task vehicles and platoon members (PMs), ensuring fair compensation for resource allocation while maintaining low latency. We introduce a personalized security layer utilizing advanced encryption standard (AES) encryption to safeguard platoon communications, a critical enhancement given the vulnerability of wireless channels. Our scheme not only proves the existence of a unique Nash equilibrium but also optimizes the utility for both task vehicles and PMs through a dynamic pricing-based Stackelberg game (DPSG) algorithm. Simulation results demonstrate that DPSG can substantially improve entire performance compared to other schemes, such as local execution, MEC offloading scheme, Hooke-Jeeves-based Stackelberg game algorithm, and reinforcement learning-based offloading optimal scheme.

I. Introduction

With the development of the Internet of Vehicles (IoV), various new applications have emerged, such as advanced intelligent assisted driving and highly automated driving map updating, which greatly enhance the user’s travel experience [1]. However, these compute-intensive and latency-sensitive applications place extremely high demands on computational and communication resource. Intelligent vehicles are equipped with a variety of sensors, such as cameras, LiDARs, and other smart devices, that offer capabilities for computation and storage [2]. Nevertheless, it is challenging for a vehicle to independently handle compute-intensive and delay-sensitive applications. Utilizing mobile edge computing (MEC) deployed roadside is an appropriate solution to solve this problem [3], [4]. MECs provide computing and storage resources to neighboring vehicles, reducing their loads by offloading partial or full tasks with low latency.