Error Estimation for Indoor 802.11 Location Fingerprinting

Lemelson, Hendrik; Kjærgaard, Mikkel Baun; Hansen, René Rydhof; King, Thomas

doi:10.1007/978-3-642-01721-6_9

Cited by 52 publications

(74 citation statements)

References 13 publications

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“…For example, Portnoy et al use clustering to detect anomalies in network traffic [27]. Clustering has been used for several purposes in localization systems: for example, Swangmuang and Krishnamurthy use it to improve performance prediction [34] and Lemelson et al use clustering as a measure for error prediction [22]. To our knowledge, clustering has not previously been used to detect erroneous user input to localization systems.…”

Section: Robustness and Clusteringmentioning

confidence: 99%

Growing an organic indoor location system

Park¹,

Charrow²,

Curtis³

et al. 2010

Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services

281

177

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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.

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Section: Robustness and Clusteringmentioning

confidence: 99%

Growing an organic indoor location system

Park¹,

Charrow²,

Curtis³

et al. 2010

Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services

281

177

View full text Add to dashboard Cite

show abstract

“…Lemelson et al use unweighted Gaussian overlap -not to localize -but to anticipate the likely estimate localization error for a given point [21]. They show that points with very similar fingerprints (as determined by the overlap function) tend to have poor localization accuracy, because they are often confused with adjacent points.…”

Section: Related Workmentioning

confidence: 99%

Molé: a scalable, user-generated WiFi positioning engine

Ledlie

Park

Curtis

et al. 2012

Journal of Location Based Services

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Abstract-We describe the design, implementation, and evaluation of Molé, a mobile organic localization engine. Unlike previous work on crowd-sourced WiFi positioning, Molé uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Molé aims for a more flexible and scalable point in the design space of localization systems. Molé employs several new techniques, including a new statistical positioning algorithm to differentiate between neighboring places, a motion detector to reduce update lag, and a scalable "cloud"-based fingerprint distribution system. Molé's localization algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a place's fingerprint in a principled manner. It also allows for aggregation of fingerprints from many users and is compact enough for on-device storage. We show through end-to-end experiments in two deployments that MAO is significantly more accurate than state-of-the-art Bayesian-based localizers. We also show that non-experts can use Molé to quickly survey a building, enabling room-grained location-based services for themselves and others.

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“…As inside one region a further distinction of cells is hard to achieve because of the similar signal properties [10], the whole region is returned as a location estimate. After the collection of the training data, our algorithm performs the following steps:…”

Section: B Measurement Clusteringmentioning

confidence: 99%

Automatic identification of fingerprint regions for quick and reliable location estimation

Lemelson

Schnaufer

Effelsberg

2010

2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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Abstract-One of the drawbacks of location fingerprinting systems is the effort that is necessary to set up and update the fingerprint database. In this paper, we propose a novel approach to significantly reduce this effort. We split the area of operation into a grid of quadratic cells and then combine these cells into larger regions of similar signal properties using a clustering algorithm and a novel similarity measure. Thus, less training data is required, and it can be collected in a more efficient way: We move through the area of operation on predefined trajectories and interpolate the approximate position for each measurement. In addition, by storing only one fingerprint for each region, we reduce the computational requirements of the location fingerprinting algorithm considerably. Since the radio measurements are quite similar in such a region, it is hard to estimate the exact location within the region; thus we do not lose much accuracy by clustering. An evaluation of our approach shows that it achieves an accuracy that is sufficient for most locationbased services and at the same time reduces the effort for the collection of the training data to a mere walk of the area of operation.

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Error Estimation for Indoor 802.11 Location Fingerprinting

Cited by 52 publications

References 13 publications

Growing an organic indoor location system

Growing an organic indoor location system

Molé: a scalable, user-generated WiFi positioning engine

Automatic identification of fingerprint regions for quick and reliable location estimation

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