A Data-Driven Sequential Localization Framework for Big Telco Data
A Data-Driven Sequential Localization Framework for Big Telco Data
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DOI：
10.1109/tkde.2019.2961657
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发表时间：
2021-08
影响因子：
8.9
通讯作者：
Fangzhou Zhu;Mingxuan Yuan;Xike Xie;Ting Wang;Shenglin Zhao;Weixiong Rao;Jia Zeng
中科院分区：
文献类型：
--
作者：
Fangzhou Zhu;Mingxuan Yuan;Xike Xie;Ting Wang;Shenglin Zhao;Weixiong Rao;Jia Zeng
电信网络和移动的终端的激增带来了大量测量报告（MR）数据的快速积累。MR数据由移动的对象在连接到数据服务时生成，并存储在后端数据中心中。地理标记或定位这些MR数据被认为对电信和交通网络的分析和优化具有深远的影响。然而，MR记录是关于移动的对象的地理位置的噪声和部分观测，因此对精确的电信数据定位提出了挑战。已经有不少尝试。单点定位方法将MR记录映射到一个位置，但由于忽略了连续MR记录的时空相干性，因此精度有限。最近的努力顺序定位技术缓解了这一问题，映射一系列的MR记录的轨迹。然而，现有的解决方案通常具有对特定模型的假设，例如，移动性和信号强度分布，或者拓扑空间的先验知识，例如，道路网络，限制了实际部署。为此，我们提出了一个数据驱动的框架，以解决在顺序电信本地化的挑战。我们仅使用原始MR记录和公共第三方GPS数据集来学习移动的对象的位置和MR记录之间的相关性，不需要模型假设和先验知识。为了处理学习过程中的数据密集型工作负载，我们使用物化视图进行有效的在线本地化和轻量级索引技术进行周期性参数调整，以提高效率和可扩展性。在真实的数据上的结果表明，与需要假设模型和先验知识的最先进的顺序定位技术相比，我们的解决方案在中值定位误差方面实现了58.8%的改进，使得我们的解决方案在有效性、效率和可使用性方面具有上级优势。
The proliferation of telco networks and mobile terminals brings the accumulation of tremendous amounts of measure report(MR) data at a rapid pace. The MR data is generated by mobile objects while connecting to data services and is stored in backend data centers. To geo-tag or localize such MR data is believed to have a profound effect on the analytics and optimizations of telco and traffic networks. However, MR records are of noisy and partial observations regarding to mobile objects’ geo-locations and hence pose challenges to accurate telco data localization. There have been quite a few attempts. Single-point localization methods map a MR record to a location, but come out with limited accuracies due to the ignorance of spatiotemporal coherence of successive MR records. Recent efforts on sequential localization techniques alleviate this by mapping a sequence of MR records to a trajectory. However, existing solutions are often with assumptions on specific models, e.g., mobility and signal strength distributions, or priori knowledge on topology space, e.g., road networks, limiting the deployment in practice. To this end, we propose a data-driven framework to tackle the challenges in sequential telco localization. We solely use raw MR records and a public third-party GPS dataset for the learning of the correlations between mobile objects’ locations and MR records, requiring no model assumptions and priori knowledge. To handle the data-intensive workloads during the learning process, we use materialized views for efficient online localization and light-weighted indexing techniques for periodical parameters tuning, in order to improve the efficiency and scalability. Results on real data show that our solution achieves 58.8 percent improvement in median localization errors compared with state-of-art sequential localization techniques that require hypothesis models and priori knowledge, making our solution superior in terms of effectiveness, efficiency, and employability.