An Adaptive Network Coding Scheme for Multipath Transmission in Cellular-Based Vehicular Networks

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ID: 260655
2020
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Abstract
With the emergence of vehicular Internet-of-Things (IoT) applications, it is a significant challenge for vehicular IoT systems to obtain higher throughput in vehicle-to-cloud multipath transmission. Network Coding (NC) has been recognized as a promising paradigm for improving vehicular wireless network throughput by reducing packet loss in transmission. However, existing researches on NC do not consider the influence of the rapid quality change of wireless links on NC schemes, which poses a great challenge to dynamically adjust the coding rate according to the variation of link quality in vehicle-to-cloud multipath transmission in order to avoid consuming unnecessary bandwidth resources and to increase network throughput. Therefore, we propose an Adaptive Network Coding (ANC) scheme brought by the novel integration of the Hidden Markov Model (HMM) into the NC scheme to efficiently adjust the coding rate according to the estimated packet loss rate (PLR). The ANC scheme conquers the rapid change of wireless link quality to obtain the utmost throughput and reduce the packet loss in transmission. In terms of the throughput performance, the simulations and real experiment results show that the ANC scheme outperforms state-of-the-art NC schemes for vehicular wireless multipath transmission in vehicular IoT systems.
Reference Key
yin2020sensorsan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chenyang Yin;Ping Dong;Xiaojiang Du;Tao Zheng;Hongke Zhang;Mohsen Guizani;Yin, Chenyang;Dong, Ping;Du, Xiaojiang;Zheng, Tao;Zhang, Hongke;Guizani, Mohsen;
Journal sensors
Year 2020
DOI
10.3390/s20205902
URL
https://doi.org/10.3390/s20205902
Keywords
Machine learning vehicular network network coding multipath transmission

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