Improving Wireless Positioning with Look-ahead Map-Matching

Jones, Kipp; Liu, Ling; Alizadeh-Shabdiz, F.

doi:10.1109/mobiq.2007.4450983

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…The localization results in existing wardriving databases are very primitive [10], [11]. The popular approach to fingerprint WiFi beacons usually yields very rough location estimates in highly dynamic and noisy vehicular networks, with errors in tens of meters, due to the fact that only a small number of beacons can be collected by fast-moving vehicles.…”

Section: Motivationsmentioning

confidence: 99%

Online War-Driving by Compressive Sensing

Arkhipov

Zhang

et al. 2015

IEEE Trans. on Mobile Comput.

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Roadside units (RSUs) are public and personal wireless access points that can provide communications with infrastructure in ad hoc vehicular networks. We present CLOCS (Counting and Localization using Online Compressive Sensing), a novel system to retrieve both the number and locations of RSUs through wardriving. CLOCS employs online compressive sensing (CS), where received signal strength (RSS) values are recorded at runtime, and the number and location of RSUs are recovered immediately based on limited RSS readings. CLOCS also uses fine retrieval based on an expectation maximization method along the driving route. Extensive simulation results and experiments in a real testbed deployed in the campus of the University of California, Irvine confirm that CLOCS can successfully reduce the number of measurements for RSU recovery, while maintaining satisfactory counting and localization accuracy. In addition, data dissemination, time cost, and effects of different mobile scenarios using CLOCS are analyzed, and the impact of CLOCS on network connectivity is studied using Microsoft VanLan traces.

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Section: Motivationsmentioning

confidence: 99%

Online War-Driving by Compressive Sensing

Arkhipov

Zhang

et al. 2015

IEEE Trans. on Mobile Comput.

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“…The location of a taxi cab is mapped to the nearest point in the transportation network using the map-matching algorithm developed in [12]. DEFINITION 3 (NETWORK EVENT).…”

Section: Definition 2 (Transportation Network)mentioning

confidence: 99%

Inferring human mobility patterns from taxicab location traces

Ganti

Srivatsa

Ranganathan

et al. 2013

Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Taxicabs equipped with real-time location sensing devices are increasingly becoming popular. Such location traces are a rich source of information and can be used for congestion pricing, taxicab placement, and improved city planning. An important problem to enable these application is to identify human mobility patterns from the taxicab traces, which translates to being able to identify pickup and dropoff points for a particular trip. In this paper, we show that while past approaches are effective in detecting hotspots using location traces, they are largely ineffective in identifying trips (pairs of pickup and dropoff points). We propose the use of a graph theory concept -stretch factor in a novel manner to identify trip(s) made by a taxicab and show that a Hidden Markov Model based algorithm can identify trips (using real datasets from taxicab deployments in Shanghai and partially simulated datasets from Stockholm) with precision and recall of 90-94% −, a significant improvement over past approaches that result in a precision and recall of about 50-60%.

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“…2) Use the principle of wardriving [19], where the users contribute online to the LPM. The idea is that users with positioning capabilities (for instance, GPS) report their position and observations (2) to a database [20], [21], which is used to position other users. 3) Predict the fingerprints using Geographical Information System planning tools [2].…”

Section: Modeling Rss Measurements For Fingerprintingmentioning

confidence: 99%

“…Here, while passing from (19) to (20), we assumed that e j is a continuous random variable (i.e., no discontinuity in its cdf). The probability density function appears in the calculation again as a design parameter.…”

Section: Likelihood Definitions For Dynamic Estimationmentioning

confidence: 99%

Fingerprinting Localization in Wireless Networks Based on Received-Signal-Strength Measurements: A Case Study on WiMAX Networks

Bshara

Orguner

Gustafsson

et al. 2010

IEEE Trans. Veh. Technol.

156

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Abstract-This paper considers the problem of fingerprinting localization in wireless networks based on received-signal-strength (RSS) observations. First, the performance of static localization using power maps (PMs) is improved with a new approach called the base-station-strict (BS-strict) methodology, which emphasizes the effect of BS identities in the classical fingerprinting. Second, dynamic motion models with and without road network information are used to further improve the accuracy via particle filters. The likelihood-calculation mechanism proposed for the particle filters is interpreted as a soft version (called BS-soft) of the BS-strict approach applied in the static case. The results of the proposed approaches are illustrated and compared with an example whose data were collected from a WiMAX network in a challenging urban area in the capitol city of Brussels, Belgium.

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Improving Wireless Positioning with Look-ahead Map-Matching

Cited by 15 publications

References 10 publications

Online War-Driving by Compressive Sensing

Online War-Driving by Compressive Sensing

Inferring human mobility patterns from taxicab location traces

Fingerprinting Localization in Wireless Networks Based on Received-Signal-Strength Measurements: A Case Study on WiMAX Networks

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