Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, %provided by Aruba, Cisco, etc., that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps. In this paper, we present our experiences in deploying a scalable location system for such venues. We show that server-side localization is not trivial and present two unique challenges associated with this approach, namely Cardinality Mismatch and High Client Scan Latency. The "Mismatch" challenge results in a significant mismatch between the set of access points (APs) reporting a client in the offline and online phases, while the "Latency" challenge results in a low number of APs reporting data for any particular client. We collect three weeks of detailed ground truth data (~200 landmarks), from a WiFi setup that has been deployed for more than four years, to provide evidences for the extent and understanding the impact of these problems. Our analysis of real-world client devices reveal that the current trend for the clients is to reduce scans, thereby adversely impacting their localization accuracy. We analyze how localization is impacted when scans are minimal. We propose heuristics to alleviate reduction in the accuracy despite lesser scans. Besides the number of scans, we summarize the other challenges and pitfalls of real deployments which hamper the localization accuracy.
翻译:在大型公共场所实际部署基于WiFi的室内本地化的情况很少,而且远远介于大多数最先进的解决方案需要客户或基础设施方面的变化。因此,尽管这些解决方案可能具有较高的定位准确性,但由于成本和/或客户的采用原因,这些解决方案并不实用。大部分公共场所使用商业控制管理的WLAN解决方案,由阿鲁巴、思科等提供%,这既不允许客户改变,也不允许基础设施改变。事实上,对于这些网站,我们观察到高度分散的运行设备,其客户端应用程序的采用率非常低。在本文件中,我们介绍了在为这些网站部署一个可缩放的准确性系统方面我们的经验。我们展示了服务器端端端的定位并非微不足道,而是提出了与这一方法相关的两个独特的挑战,即Craintality Mismatch和高客户端扫描时间。“Mismatch”挑战导致接入点在报告客户端的离线和在线阶段的接入点之间出现严重不匹配,而“Latinity”挑战则导致客户点报告数据数量较少,对于任何客户的当前客户的准确化数据报告则会减少。我们比维realalalalalalalalalalalalalalalalalalalalalalalalalalalalation 范围要低得多。我们收集了4个星期。我们收集了4年的准确度分析。我们收集了多少了。我们收集了这些事实数据。我们收集了4年的准确度数据,我们收集了多少次。我们收集了实地数据。