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History based techniques for device management and congestion control in mobile networks.

机译:用于移动网络中设备管理和拥塞控制的基于历史的技术。

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摘要

We consider two problems in mobile computing that we approach through using historical data to develop relevant predictors.;We first present machine learning based algorithms by which a cell phone can discern that it may be lost, and take steps to enhance its chances of being recovered. We use data collected from the Reality Mining project to create a suite of test cases that model lost cell phone behavior. On these data sets our best algorithms can identify cases of a lost mobile device, based on its behavior over the previous 3 hours, with close to 100% accuracy.;We then study the problem of congestion control in DTN. A strategy consists of a drop policy and an algorithm that attempts to avoid congestion events. We introduce a simple drop policy, REJECT, that has not been considered in prior work, and a congestion avoidance algorithm: TISS.;In simulation experiments on popular archived real-life data sets, we show that REJECT can substantially improve delivery rates over prior techniques that focus on receiving nodes rejecting either incoming messages or messages held in their buffers. We refer to such strategies as EJECT. For two of the most important DTN routing algorithms, REJECT increases the delivery rate by 28.96% over EJECT. REJECT works exceptionally well when the DTN interaction graph is well connected, which causes earlier policies to eject large numbers of messages. In that case it outperforms EJECT by 55.78% in delivery rate.;We conduct an extensive experimental evaluation of our algorithm. We consider TISS when applied to the two DTN routing algorithms. In addition we modified other approaches to Congestion Control, Storage Routing (SR) and Fair Routing (FR), so as to be able to compare fairly (for those algorithms) to their core ideas. We show that the combination of [REJECT, TISS] is the best overall strategy to improve the delivery rate. It improves over [EJECT, No Congestion Avoidance] by 31.89%. In comparison, our (advantageously) modified versions of two other approaches to congestion control [REJECT, SR] and [REJECT, FR] improve by 24.16% and 27.57%, respectively.
机译:我们考虑了移动计算中的两个问题,这些问题是我们通过使用历史数据来开发相关的预测变量而解决的;我们首先提出了基于机器学习的算法,通过该算法,手机可以识别出手机可能会丢失,并采取措施来提高其被恢复的机会。我们使用从Reality Mining项目中收集的数据来创建一套测试用例,以模拟丢失的手机行为。在这些数据集上,我们的最佳算法可以根据其在过去3个小时内的行为识别丢失的移动设备的情况,准确率接近100%。;然后,我们研究了DTN中的拥塞控制问题。策略由丢弃策略和尝试避免拥塞事件的算法组成。我们介绍了一个简单的丢弃策略REJECT,该策略在以前的工作中并未考虑过;拥塞避免算法是TISS 。;在对流行的已归档现实生活数据集进行的模拟实验中,我们证明REJECT可以大大提高之前的交付速度专注于接收节点拒绝传入消息或保留在其缓冲区中的消息的技术。我们将这种策略称为EJECT。对于两种最重要的DTN路由算法,与EJECT相比,REJECT可以将传递速率提高28.96%。当DTN交互图连接良好时,REJECT会特别出色,这会导致较早的策略弹出大量消息。在这种情况下,它的交付率要比EJECT高出55.78%。;我们对该算法进行了广泛的实验评估。我们将TISS应用于两种DTN路由算法。此外,我们修改了其他方法来处理拥塞控制,存储路由(SR)和公平路由(FR),以便能够(对于那些算法)公平地比较其核心思想。我们证明[REJECT,TISS]的组合是提高交货率的最佳整体策略。与[EJECT,无拥塞避免]相比,它提高了31.89%。相比之下,我们的(有利地)其他两种拥塞控制方法[REJECT,SR]和[REJECT,FR]的改进版本分别提高了24.16%和27.57%。

著录项

  • 作者

    Zhang, Chi.;

  • 作者单位

    Polytechnic Institute of New York University.;

  • 授予单位 Polytechnic Institute of New York University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 115 p.
  • 总页数 115
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:40:50

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