Accuracy characterization for metropolitan-scale Wi-Fi localization

By Yu-Chung Cheng, Yatin Chawathe, Anthony LaMarca, John Krumm. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2005

538 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/0ed02a9c2db5789d4bf73ae088dfcaf7cb39038f

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Citations in the corpus

You are facing the Mona Lisa: spot localization using PHY layer information (2012, background): “Noting the difﬁculties of installing special hardware, RADAR, Place Labs and Horus [4, 6, 8] explored the feasibility of using signal strengths from existing WiFi APs.”
WILL: Wireless indoor localization without site survey (2012, methodology): “Most of these techniques utilize the RF signals such as [9], [10], [11], [12], [13], [14], [15].”
BreadCrumbs: forecasting mobile connectivity (2008, background): “The authors of PlaceLab found an average error of ±20 or 30 meters for their technique, as compared to GPS.”
RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers (2009, methodology): “A number of variants on probabilistic Bayesian inference approaches have appeared in the literature [4], [7], [20]–[28].”
Practical Metropolitan-Scale Positioning for GSM Phones (2006, methodology): “In contrast to prior experiments on WiFi density that effectively simulate devices with slower scanning rates [4], we are interested in spatial thinning that simulates the effect of driving fewer streets.”
Vehicular Ad Hoc Networks: A New Challenge for Localization-Based Systems (2008, methodology): “A Localization Service can be implemented using any known infrastructured localization system such as the Cricket Location-Support System [35], RADAR [36], Ultra-Wideband Localization [37], or WiFi Localization [38,39].”
SurroundSense: mobile phone localization via ambience fingerprinting (2009, background): “While creative WiFi fingerprinting techniques exist [3,4], they”
Extracting a Mobility Model from Real User Traces (2006, methodology): “If a map of access points is not available, one can collect location of APs through methods such as war-driving (as used by Place Lab [10], [11]), or using AP position-estimation techniques [12].”
Zee: zero-effort crowdsourcing for indoor localization (2012, background): “Several other improvements over and extensions of the basic RF fingerprinting based localization have been proposed, such as the incorporation of mobility constraints [12] and an extension to outdoor settings [8].”
SensLoc: sensing everyday places and paths using less energy (2010, background): “Response rate is the ratio of the detection count and the total number of scans for each beacon, and has been found to be more robust in predicting distance than signal strength [4].”
VTrack: accurate, energy-aware road traffic delay estimation using mobile phones (2009, methodology): “Another approach is to use GPS samples as ground truth [6].”
Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning (2009, methodology): “K-nearest neighbor estimation has been most widely used, in part due to its computational simplicity and in part because it performs well relative to other techniques (Lin and Lin 2005) Most of the knowledge on the performance of WiFi positioning has been gained from studies in well-controlled…”
Micro-Blog: sharing and querying content through mobile phones and social participation (2008, background): “WiFi based localization [9] can improve battery lifetime at the expense of higher localization error, and is limited to dense war-driven urban areas.”