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.
Fast and Reliable WiFi Fingerprint Collection for Indoor Localization
University of Melbourne（墨尔本大学）；China University of Geosciences（中国地质大学）；National Engineering Research Center for Geographic Information System（国家地理信息系统工程技术研究中心）
基于信道状态信息（Channel State Information，CSI）的指纹识别技术在WIFI室内定位中得到了广泛的关注，频率多样且时间稳定的CSI比粗接收信号强度（Recovered Signal Strength，RSS）更能反映位置相关的信道特性。然而，CSI的获取需要接入点（AP）的合作并且仅涉及多个帧，这对现实世界的部署施加了限制。在本文中，我们提出了CRISLoc，第一个CSI指纹定位原型系统使用无处不在的智能手机。CRISLoc在一个完全被动的模式下运行，在他自己的CSI采集过程中偷听数据包。通过校准WiFi放大器电路施加的失真来净化智能手机CSI。CRISLoc通过联合聚类和离群值检测方法来解决改变AP的挑战。提出了一种新的迁移学习方法，在过时的指纹和一些新的测量的基础上重建高维CSI指纹数据库，并提出了一种增强的KNN方法来精确定位智能手机的位置。我们的研究揭示了智能手机CSI的稳定性和灵敏度的重要属性，这是以前没有报道过的。实验结果表明，CRISLoc在6m × 8m的研究实验室中可以实现约0.29m的平均误差。移动一个和两个AP后，平均误差分别增加了5.4 cm和8.6 cm，这验证了CRISLoc对环境变化的鲁棒性。
RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth
Marconi-Rosenblatt AI/ML Innovation Lab, ANDRO Computational Solutions, LLC（ANDRO计算解决方案有限责任公司马可尼-罗森布拉特人工智能/机器学习创新实验室）；Institute of Wireless Internet of Things, Northeastern University（东北大学无线物联网研究所）；University at Buffalo（布法罗大学）
Collaborative SLAM based on Wifi Fingerprint Similarity and Motion Information
Singapore University of Technology and Design（新加坡科技设计大学）；Southwest University of Science and Technology（西南科技大学）；University of Moratuwa（莫拉图瓦大学）；Nanyang Technological University（南洋理工大学）
Decentralized Indoor Localization Based on A Sparse Gaussian Process with Reduced-Dimensional Inputs for Real-Time Sensing and Training on IoT Devices
Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院网络空间安全研究所）；Binjiang Institute of Artificial Intelligence, Zhejiang University of Technology（浙江工业大学滨江人工智能研究院）；School of Artificial Intelligence, Suzhou Institute of Industrial Technology（苏州工业职业技术学院人工智能学院）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；University of Liverpool（利物浦大学）；D5 Data Co., Ltd.（D5数据有限公司）
