Cooperative computational offloading in mobile edge computing for vehicles: A model-based DNN approach
Document Type
Journal Article
Publication Title
IEEE Transactions on Vehicular Technology
Publisher
IEEE
School
School of Engineering
RAS ID
52766
Funders
Beijing Natural Science Foundation - Grant number: 4212015
China Ministry of Education - China Mobile Scientific Research Foundation - Grant number: MCM20200102
Deanship of Scientific Research, King Khalid University
Large Groups Project - Grant number: RGP.2/201/43
Abstract
Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.
DOI
10.1109/TVT.2022.3217323
Access Rights
subscription content
Comments
Munawar, S., Saleem, O., Ali, Z., Waqas, M., Tu, S., Hassan, S. A. & Abbas, G. (2023). Cooperative computational offloading in mobile edge computing for vehicles: A model-based DNN approach. IEEE Transactions on Vehicular Technology, 72(3), 3376-3391.
https://doi.org/10.1109/TVT.2022.3217323