• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Robust Trajectory Estimation for Crowdsourcing-Based Mobile Applications
【24h】

Robust Trajectory Estimation for Crowdsourcing-Based Mobile Applications

机译：基于众包的移动应用的鲁棒轨迹估计

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations.
机译：近年来，随着配备各种内置传感器的智能手机迅速普及，基于众包的移动应用程序变得越来越普遍。大量的众包感知数据刺激了研究人员完成某些过去曾经昂贵或不可能完成的任务，但是众包数据的质量（这一点非常重要）并未得到足够的重视。实际上，低质量的众包数据易于包含异常值，这可能严重损害众包应用程序。因此，在这项工作中，我们考虑众包数据质量进行了先驱调查。具体来说，我们专注于估算用户运动轨迹信息，该信息在多个众包应用（例如室内定位，上下文识别，室内导航等）中起着至关重要的作用。我们诉诸于稳健的统计数据家族，并设计了稳健的轨迹估算方案， TrMCD，它可以减轻异常众包用户轨迹的负面影响，将正常用户与异常用户区分开，并克服众包轨迹空间不平衡带来的挑战。进行了两个真实的实验，结果表明TrMCD在估计用户运动轨迹和将指纹映射到物理位置方面既强大又有效。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号