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Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing

by and *
Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Sensors 2020, 20(23), 6820; https://doi.org/10.3390/s20236820
Received: 15 October 2020 / Revised: 21 November 2020 / Accepted: 24 November 2020 / Published: 29 November 2020
(This article belongs to the Special Issue Edge-Based AI for the Internet of Things)
To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC’s computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms’ effectiveness is verified with a host of numerical simulation results from different perspectives. View Full-Text
Keywords: vehicular cloud computing; remote cloud computing; long short term memory network; deep reinforcement learning; computation offloading; vehicular network vehicular cloud computing; remote cloud computing; long short term memory network; deep reinforcement learning; computation offloading; vehicular network
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MDPI and ACS Style

Xu, S.; Guo, C. Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing. Sensors 2020, 20, 6820.

AMA Style

Xu S, Guo C. Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing. Sensors. 2020; 20(23):6820.

Chicago/Turabian Style

Xu, Shilin; Guo, Caili. 2020. "Computation Offloading in a Cognitive Vehicular Networks with Vehicular Cloud Computing and Remote Cloud Computing" Sensors 20, no. 23: 6820.

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