Zero-Configuration, Robust Indoor Localization: Theory and Experimentation

By Hyuk Lim, Lu-Chuan Kung, Jennifer C. Hou, Haiyun Luo. Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications, 2006

322 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Ocean Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/caf9900a8f4dc812e5bfa276187fefc1981dfa5b

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In Electrical and Electronic Engineering

1. Yunhao Liu
2. Zheng Yang
3. Moustafa Youssef
4. Zimu Zhou
5. Baris Fidan
6. Guoqiang Mao
7. Wei Xi
8. Jie Xiong

In Ocean Engineering

1. Yunhao Liu
2. Zheng Yang
3. Zimu Zhou
4. Moustafa Youssef
5. V. Padmanabhan
6. Krishna Chintalapudi
7. Matthew Mah
8. Wei Xi

In Signal Processing

1. Yunhao Liu
2. Zheng Yang
3. Zimu Zhou
4. Moustafa Youssef
5. Guoqiang Mao
6. Baris Fidan
7. Shaojie Tang
8. Xue Liu

Citations in the corpus

• From RSSI to CSI (2013, background): “Other empirical studies have also shown that RSSI readings vary at both small (seconds) and large (hours) granularities and can be as high as 7 dB in a typical student cubicle [Lim et al. 2006].”
• Challenges: device-free passive localization for wireless environments (2007, background): “Many systems over the years have tackled the localization problem including the GPS [3] system, infrared [13], ultrasonic [10], and radio frequency (RF) [2,8,16,21].”
• Growing an organic indoor location system (2010, unknown): “[23].”
• ArrayTrack: A Fine-Grained Indoor Location System (2011, unknown): “Most existing systems choose the former, building maps of multipath signal strength [4, 5, 32, 25], or estimating coarse differences using RF propagation models [9, 11].”
• A survey on sensor localization (2010, methodology): “bility. Efforts were expended to obtain the mapping between RSSI measurements and the associated distances to capture the impact of multipath fading and environmental variations on RSSI measurements in the indoor space [ 12 ].”
• Rendered Path: Range-Free Localization in Anisotropic Sensor Networks With Holes (2007, methodology): “The global algorithms localize all nodes simultaneously from the globally collected distance or angular information [2, 12, 14 , 21].”
• SpotFi: Decimeter Level Localization Using WiFi (2015, methodology): “RSSI based approaches: This class of systems measures the RSSI from the target at multiple APs, combines them via triangulation along with a propagation model to locate the target [3, 10, 4, 5, 11, 12, 13].”
• Redpin - adaptive, zero-configuration indoor localization through user collaboration (2008, methodology): “32 meters using only WiFi RSS [22], we were confident to be able to correctly map the combination of all readings to a location in almost every case.”
• Indoor localization without the pain (2010, background): “[17] employ WiFi sniffers at known locations.”
• Electronic frog eye: Counting crowd using WiFi (2014, methodology): “RSS-based approaches can be categorized into the Radio Propagation Model [4], [19] and the Fingerprint Method [20], [21], [5].”
• Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, background): “The algorithms selected here span broad techniques that proved practical and useful from surveying the references (Elnahrawy et al., 2004; Bahl et al., 2000; Youssef et al., 2003; Youssef et al., 2004; Battiti et al., 2002; Moore et al., 2004; Fang et al., 2005; Savvides et al., 2001; Lorincz et al.”