Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing

Xiting Peng (), Yandi Zhang, Xiaoyu Zhang, Chaofeng Zhang and Wei Yang
Additional contact information
Xiting Peng: School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
Yandi Zhang: School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
Xiaoyu Zhang: Liaoning Liaohe Laboratory, Shenyang 110033, China
Chaofeng Zhang: School of Information and Electronic Engineering, Advanced Institute of Industrial Technology, Tokyo 140-0011, Japan
Wei Yang: School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China

Mathematics, 2024, vol. 12, issue 23, 1-17

Abstract: The advancement of the Internet of Autonomous Vehicles has facilitated the development and deployment of numerous onboard applications. However, the delay-sensitive tasks generated by these applications present enormous challenges for vehicles with limited computing resources. Moreover, these tasks are often interdependent, preventing parallel computation and severely prolonging completion times, which results in substantial energy consumption. Task-offloading technology offers an effective solution to mitigate these challenges. Traditional offloading strategies, however, fall short in the highly dynamic environment of the Internet of Vehicles. This paper proposes a task-offloading scheme based on deep reinforcement learning to optimize the strategy between vehicles and edge computing resources. The task-offloading problem is modeled as a Markov Decision Process, and an improved twin-delayed deep deterministic policy gradient algorithm, LT-TD3, is introduced to enhance the decision-making process. The integration of LSTM and a self-attention mechanism into the LT-TD3 network boosts its capability for feature extraction and representation. Additionally, considering task dependency, a topological sorting algorithm is employed to assign priorities to subtasks, thereby improving the efficiency of task offloading. Experimental results demonstrate that the proposed strategy significantly reduces task delays and energy consumption, offering an effective solution for efficient task processing and energy saving in autonomous vehicles.

Keywords: vehicular edge computing; Internet of Autonomous Vehicles; deep reinforcement learning; Markov decision (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/23/3820/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/23/3820/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:23:p:3820-:d:1535299

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().