A Novel Deep Reinforcement Learning-based Approach for Task-offloading in Vehicular Networks

S. M.Ahsan Kazmi, Safa Otoum, Rasheed Hussain, Hussein T. Mouftah

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

7 Citations (Scopus)

Abstract

Next-generation vehicular networks will impose unprecedented computation demand due to the wide adoption of compute-intensive services with stringent latency requirements. Computational capacity of vehicular networks can be enhanced by integration of vehicular edge or fog computing; however, the growing popularity and massive adoption of novel services make edge resources insufficient. This challenge can be addressed by utilizing the onboard computation resources of neighboring vehicles that are not resource-constrained along with the edge computing resources. To fill the gaps, in this paper, we propose to solve the problem of task offloading by jointly considering the communication and computation resources in a mobile vehicular network. We formulate a non-linear problem to minimize the energy consumption subject to the network resources. Further-more, we consider a practical vehicular environment by taking into account the dynamics of mobile vehicular networks. The formulated problem is solved via a deep reinforcement learning (DRL) based approach. Finally, numerical evaluations are performed that demonstrates the effectiveness of our proposed scheme.

Original languageEnglish
Title of host publication2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-7281-8104-2
ISBN (Print)978-1-7281-8105-9
DOIs
Publication statusPublished - 2021
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: 7 Dec 202111 Dec 2021

Publication series

Name2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings

Conference

Conference2021 IEEE Global Communications Conference, GLOBECOM 2021
Country/TerritorySpain
CityMadrid
Period7/12/2111/12/21

Bibliographical note

Funding Information:
This research was supported by the Faculty of Technological Innovation, Zayed University, under grant number R20130.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Next-generation vehicular network
  • task offloading
  • vehicle to vehicle resource sharing

Fingerprint

Dive into the research topics of 'A Novel Deep Reinforcement Learning-based Approach for Task-offloading in Vehicular Networks'. Together they form a unique fingerprint.

Cite this