Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles

Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles

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  • Ιούλιος 8, 2022
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Dimitrios Pliatsios, Panagiotis Sarigiannidis, Thomas D Lagkas, Vasileios Argyriou, Alexandros-Apostolos A Boulogeorgos, Peristera Baziana: Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles. In: IEEE Transactions on Green Communications and Networking, 6 (3), pp. 1468-1480, 2022, ISSN: 2473-2400.

Περίληψη

The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.

BibTeX (Download)

@article{9820768,
title = {Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles},
author = {Dimitrios Pliatsios and Panagiotis Sarigiannidis and Thomas D Lagkas and Vasileios Argyriou and Alexandros-Apostolos A Boulogeorgos and Peristera Baziana},
url = {https://www.researchgate.net/publication/361864374_Joint_Wireless_Resource_and_Computation_Offloading_Optimization_for_Energy_Efficient_Internet_of_Vehicles},
doi = {10.1109/TGCN.2022.3189413},
issn = {2473-2400},
year  = {2022},
date = {2022-07-08},
journal = {IEEE Transactions on Green Communications and Networking},
volume = {6},
number = {3},
pages = {1468-1480},
abstract = {The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.},
keywords = {6G, B5G, block coordinate descent, Computation offloading},
pubstate = {published},
tppubtype = {article}
}
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