Growing an organic indoor location system

By Jun-geun Park, Benjamin Charrow, Dorothy W. Curtis, Jonathan Battat, E. Minkov, Jamey Hicks, Seth J. Teller, Jonathan Ledlie. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2010

333 citations recorded by the source corpus.

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.

Subfields: Computer Networks and Communications, Computer Science Applications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7775250ba1626d0a0f47bc66ec05f77ad3252cde

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Citations in the corpus

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, methodology): “In the face of these challenges, Park et al. [103] use Voronoi regions for reasoning about gaps in coverage (i.e., areas with low density of ﬁngerprints) and a clustering method for identifying potentially erroneous user data.”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “Received Signal Strength (RSS) is the most commonly used signal feature for WiFi-based indoor localization [Bahl and Padmanabhan 2000; Youssef and Agrawala 2005; Chintalapudi et al. 2010; Park et al. 2010; Goswami et al. 2011].”
Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “Explicit crowdsourcing-based data collection: In many recent works, crowdsourcing-based approaches [130], [135] have been proposed to replace the professional site survey with explicit and unprofessional user participation.”
WILL: Wireless indoor localization without site survey (2012, methodology): “OIL [7] structures an organic indoor localization system by using Voronoi regions for conveying uncertainty and employing a clustering method for identifying potentially erroneous user data.”
Smartphones Based Crowdsourcing for Indoor Localization (2015, methodology): “Most of these techniques utilize the RF signals such as RADAR [2], Horus [40], improved upon RADAR, LANDMARC [21], ActiveCampus [11], PlaceLab [16] and OIL [25].”
Indoor Positioning Technologies (2012, methodology): “In order to avoid the computational burden intrinsic to ray tracing Parodi et al. (2006) used the Dominant Path Model (DPM) which takes only into account the strongest path which is not necessarily identical to the direct path.”
UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems (2014, background): “Other WLAN-based works as [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] have been not included in Table I since they do not provide information about the characteristics of the database or the information provided shows that their databases are very s”
Phaser: enabling phased array signal processing on commodity WiFi access points (2014, background): “Other approaches leverage ray tracing [7] or human intervention (either by manual location input [15], or a user’s spinning movement [21]) to obviate the need for manual radio map construction.”
Indoor localization without infrastructure using the acoustic background spectrum (2011, background): “Recent localization work has explored strategies for building fingerprint databases on-line rather than relying on explicit surveys [24].”
Locating in fingerprint space: wireless indoor localization with little human intervention (2012, methodology): “Most of these techniques utilize the RF signals such as RADAR [2], Horus [35], improved upon RADAR, LANDMARC [20], ActiveCampus [11], PlaceLab [15] and OIL [23].”