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Ubiquitous Sensor Networks Traffic Models for Medical and Tracking Applications

机译:用于医疗和跟踪应用的无处不在的传感器网络流量模型

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摘要

The Internet of Things (IoT) is a new concept for telecommunication development. The Ubiquitous sensor Network (USN) is one of the general IoT components. The traffic models for such network should be studied well. The USN traffic models study results for medical and tracking applications are considered in this paper. The paper results show that the traffic flows for medical and tracking USN applications are self-similar with the middle level of self-similarity in both cases. The R/S and Higuchi methods were used for Hurst parameter estimation. The Hurst parameter dependence on the length of interval between packets for medical USN and the Hurst parameter dependence on the packet rate and OFF perion length for tracking USN are considered.
机译:物联网(IoT)是电信发展的新概念。泛在传感器网络(USN)是通用的IoT组件之一。应该很好地研究这种网络的流量模型。本文考虑了用于医疗和跟踪应用的USN交通模型研究结果。纸上的结果表明,在两种情况下,医学和跟踪USN应用程序的流量都是自相似的,中间水平是自相似的。 R / S和Higuchi方法用于Hurst参数估计。考虑用于医疗USN的分组之间的间隔长度的Hurst参数依赖性以及用于跟踪USN的取决于分组速率和OFF周期长度的Hurst参数依赖性。

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