Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poo...Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational costs.In this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a subgraph.Then,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural networks.This approach offers high accuracy,easy scalability to new buildings,and computational efficiency.Experimental results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art methods.Notably,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.展开更多
Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we ...Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone＇s sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the eiTiciency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.展开更多
基金supported by the National Natural Science Foundation of China(No.42174050,62172066,62172064,62322601)National Science Foundation for Excellent Young Scholars(No.62322601)+5 种基金Open Research Projects of Zhejiang Lab(No.K2022NB0AB07)Venture&Innovation Support Program for Chongqing Overseas Returnees(No.cx2021047)Chongqing Startup Project for Doctorate Scholars(No.CSTB2022BSXM-JSX005)Excellent Youth Foundation of Chongqing(No.CSTB2023NSCQJQX0025)China Postdoctoral Science Foundation(No.2023M740402)Fundamental Research Funds for the Central Universities(No.2023CDJXY-038,2023CDJXY-039).
文摘Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational costs.In this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a subgraph.Then,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural networks.This approach offers high accuracy,easy scalability to new buildings,and computational efficiency.Experimental results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art methods.Notably,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.
基金This work was supported by the National High Technology Research and Development 863 Program of China under Grant No. 2013AA01A213 and the National Natural Science Foundation of China under Grant Nos. 91318301, 61373011 and 61321491.
文摘Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone＇s sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the eiTiciency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.
