Data-Driven Energy Efficient Predictive Resource Allocation in Internet of Vehicles

Na Xue, Haixia Zhang, Chuanting Zhang, Tiantian Li, Dongfeng Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


In Internet of Vehicles (IoV), the high mobility of vehicles aggravates the uneven and dynamic spatial-temporal distribution of wireless traffic, leading to low resource utilization. To improve the wireless resource utilization efficiency of IoV, this paper investigates predictive resource allocation strategy by exploiting vehicle mobility information. To characterize vehicle's speed distribution, we adopt a kernel density estimation method to analyze the real trajectory dataset. Based on this analysis, we propose an iterative predictive resource allocation scheme considering different mobility patterns and channel distribution information. Simulation results demonstrate that our proposed scheme converges well and can obtain considerable performance gains over non-predictive resource allocation schemes.
Original languageEnglish (US)
Title of host publication2020 International Conference on Wireless Communications and Signal Processing (WCSP)
Number of pages6
ISBN (Print)9781728172361
StatePublished - Oct 21 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-02-04
Acknowledgements: This work was supported in part by Project of International Cooperation and Exchanges NSFC under Grant No. 61860206005, and in part by the National Natural Science Foundation of China under Grant No. 61671278.


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