Machine learning based code dissemination by selection of reliability mobile vehicles in 5G networks
Machine learning based code dissemination by selection of reliability mobile vehicles in 5G networks
复制标题
通过选择 5G 网络中的可靠性移动车辆进行基于机器学习的代码传播
DOI：
10.1016/j.comcom.2020.01.034
复制
发表时间：
2020-02
影响因子：
6
通讯作者：
Wong Kelvin Kian Loong
中科院分区：
文献类型：
--
作者：
Li Ting;Zhao Ming;Wong Kelvin Kian Loong
最近，5G网络的演进被预见为未来移动的车辆社交网络（VSN）的主要驱动力，其可以提供新的代码传播方法。基于这一概念，车辆可以用作代码传播者。也就是说，可以通过接收由VSN中的车辆传播的更新的程序代码来升级智能城市的基础设施。具体来说，5G网络中的车辆很难管理。在这个领域中，程序代码的安全性是一个关键的挑战。同时，提高程序代码的覆盖率也是一个挑战。然而，安排大量的车辆作为代码传播者将产生大量的地面控制站（GCS）的成本。因此，本文利用机器学习方法，提出了"基于机器学习的5G网络可靠性移动的车辆选择码分发"（MLCD）方案，选择可靠度和覆盖率较高的车辆作为码分发者，以较低的成本分发码。首先，计算并选择车辆的可靠度，以提高代码传播的安全度。其次，优先选择覆盖率较高的车辆，承诺代码覆盖率。第三，利用机器学习方法选择覆盖率和可靠度都较高的车辆作为代码传播者，成本有限。与随机选择方案和仅覆盖方案相比，MLCD方案在5G网络中可以将代码分发过程的安全度提高83.6%和18.86%，并且可以将更新信息的覆盖率提高23.16%。综合性能分别提高了80.56%和17.25%。未来的工作重点是通过更先进和合适的机器学习方法来提高5G网络中的代码安全性。
Recently, the evolving of 5G networks is foreseen as a major driver of future mobile vehicular social networks (VSNs), which can provide a novel method of code disseminations. Based on this concept, vehicles can be used as code disseminators. That is, infrastructures of a smart city can be upgraded by receiving updated program codes that are disseminated by vehicles in the VSNs. Specifically, vehicles in the 5G network are hard to be managed. Under this domain, safety of program codes is a key challenge. Meanwhile, improving coverage of program codes is also challenging. However, arranging plenty of vehicles as code disseminators will incur large costs of the ground control station (GCS). Therefore, by utilizing machine learning methods, this paper proposes a “Machine Learning based Code Dissemination by Selecting Reliability Mobile Vehicles in 5G Networks” (MLCD) scheme to choose vehicles with higher reliable degree and coverage ratio as code disseminators to deliver code with lower costs. Firstly, reliable degrees of vehicles are calculated and selected to improve safety degree of code disseminations. Secondly, vehicles with higher coverage ratio are preferred to promise code coverage. Thirdly, machine learning methods are utilized to select vehicles with both higher coverage ratios and reliable degrees as code disseminators with limited costs. Compared to random-selection and coverage-only scheme respectively, the MLCD scheme can improve safety degree of code dissemination process by 83.6% and 18.86% in 5G networks, and can improve coverage ratio of updated information by 23.16%. Comprehensive performances of the proposed scheme can be improved by 80.56% and 17.25% respectively. Future works focus on improving code security in 5G networks by more advanced and suitable machine learning methods.
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