Position-based services (PBSs) that deliver networked amenities based on roaming user's positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in PBSs. For effective PBSs, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques; however, only few deliberate the progress of position-based real-time applications considering significant tasks of PBSs. In this paper, a method for conserving position confidentiality of roaming PBSs users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor and estimates user destination along with the position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm, which will ensure the timely delivery of PBSs. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted, and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBSs.

中文翻译：

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在实时工业信息学中通过移动边缘计算通过机器学习范式加强基于位置的机密性

基于漫游用户的位置来提供网络便利的基于位置的服务（PBS）随着智能移动设备的传播而逐渐流行。位置是PBS中的重要情况之一。对于有效的PBS，提取和识别有意义的位置以及估计随后的位置是基本过程。一些研究人员和从业人员试图使用各种技术来识别和预测位置。但是，考虑到PBS的重要任务，很少有人考虑基于位置的实时应用程序的进展。本文提出了一种使用机器学习技术保护漫游PBS用户的位置机密性的方法。对于漫游PBS用户，我们建议分三个阶段进行。它通过合并决策树和k近邻来识别用户位置，并使用隐藏的马尔可夫模型估算用户目的地以及位置跟踪序列。此外，在所提出的范例中遵循了移动边缘计算服务策略，这将确保PBS的及时交付。移动边缘服务策略的好处是通过漫游用户附近的网络和计算服务提供位置保密性和低延迟。进行了全面的实验，证实了所提出的方法在PBS中达到了90％以上的位置机密性。这将确保及时交付PBS。移动边缘服务策略的好处是通过漫游用户附近的网络和计算服务提供位置保密性和低延迟。进行了彻底的实验，证实了所提出的方法在PBS中达到了90％以上的位置机密性。这将确保及时交付PBS。移动边缘服务策略的好处是通过漫游用户附近的网络和计算服务提供位置保密性和低延迟。进行了彻底的实验，证实了所提出的方法在PBS中达到了90％以上的位置机密性。