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Computing on Wheels: A Deep Reinforcement Learning-Based Approach

Ahsan Kazmi, S. M.; Ho, Tai Manh; Nguyen, Tuong Tri; Fahim, Muhammad; Khan, Adil; Piran, Md Jalil; Baye, Gaspard


S. M. Ahsan Kazmi

Tai Manh Ho

Tuong Tri Nguyen

Muhammad Fahim

Md Jalil Piran

Gaspard Baye


Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporating vehicular edge or fog computing paradigm. However, the growing popularity and massive adoption of novel services make the edge resources insufficient. A possible solution to overcome this challenge is to employ the onboard computation resources of close vicinity vehicles that are not resource-constrained along with the edge computing resources for enabling tasks offloading service. In this paper, we investigate the problem of task offloading in a practical vehicular environment considering the mobility of the electric vehicles (EVs). We propose a novel offloading paradigm that enables EVs to offload their resource hungry computational tasks to either a roadside unit (RSU) or the nearby mobile EVs, which have no resource restrictions. Hence, we formulate a non-linear problem (NLP) to minimize the energy consumption subject to the network resources. Then, in order to solve the problem and tackle the issue of high mobility of the EVs, we propose a deep reinforcement learning (DRL) based solution to enable task offloading in EVs by finding the best power level for communication, an optimal assisting EV for EV pairing, and the optimal amount of the computation resources required to execute the task. The proposed solution minimizes the overall energy for the system which is pinnacle for EVs while meeting the requirements posed by the offloaded task. Finally, through simulation results, we demonstrate the performance of the proposed approach, which outperforms the baselines in terms of energy per task consumption.


Ahsan Kazmi, S. M., Ho, T. M., Nguyen, T. T., Fahim, M., Khan, A., Piran, M. J., & Baye, G. (2022). Computing on Wheels: A Deep Reinforcement Learning-Based Approach. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22535-22548.

Journal Article Type Article
Acceptance Date Apr 4, 2022
Online Publication Date Apr 15, 2022
Publication Date Nov 1, 2022
Deposit Date May 7, 2024
Publicly Available Date Jul 9, 2024
Journal IEEE Transactions on Intelligent Transportation Systems
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 11
Pages 22535-22548
Keywords Next-generation intelligent transport system; Task offloading; Vehicle-to-vehicle communication; Deep reinforcement learning
Public URL
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Accepted manuscript (2.4 Mb)

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