Loceva - an evaluation tool for 802.11-based positioning systems.
Loceva is an evaluation tool for 802.11-based positioning systems. Loceva uses trace files generated by Loctrace to evaluate different kinds of positioning algorithms. A large number of state-of-the-art positioning algorithms are supported by Loceva. Loceva contains a lot of filters and generators to set up different scenarios and enable emulation.
loceva-0.5.1.src.tar.gz, loceva-0.5.1.tar.gz, property.prop.txt
the initial version.
king-tools The Loceva website
802.11 GPS location signal strength
This tool is released under the GNU General Public License. Please respect our work and abide the license.
See "usage" for details.
See "usage" for details.
Loceva can be controlled by a so-called property file. In Java a property file contains key-value-pairs with a equals character as seperator. Most configurable values of Loceva are accessible by properties so that the same jar file can be used to emulate a wide range of different scenarios. You can download an example property file that can be used to play around with Loceva. After downloading and unpacking the jar archive Loceva can be run with the following command: java -cp loceva-0.5.1.jar:locutil1-0.5.1.jar:locutil2-0.5.2.jar org.pi4.loceva.Loceva -offline FILENAME -online FILENAME [-prop PROPERTY] FILENAME can be a trace file containing offline traces as well as online traces. Both parameters -offline and -online are required. The -prop parameter can be used optionally to define a property file.
1. Overview Trace files generated by Loctrace are used by Loceva to evaluate different kinds of positioning algorithms. A large number of state-of-the-art positioning algorithms are supported by Loceva. Loceva contains a lot of filters and generators to set up different scenarios and enable emulation. 2. Management To make it easy to evaluate and compare algorithms currently under research, Loceva contains a management part that enables emulation in general and allows to easily select different kinds of scenarios. For this, Loceva utilizes trace files created with Loctrace to emulate a specific scenario. Such an emulated scenario can then be used for a comparison of different positioning algorithms. This makes sure that differences in the results are based on the positioning algorithms and not on the environment that changed over time in a way beneficially for one particular algorithm. The creation and management of various scenarios is enabled by so-called filters. Filters create different scenarios by disabling or selecting different objects of a trace file. For instance, a MAC filter artificially switches off access points even if they have been part of the trace file. Another example is the position filter that disables different reference points of the fingerprint database based on their coordinates. 3. Algorithms The positioning part contains various positioning algorithms to make it easy to compare newly envisioned algorithms with state-of-the-art ones. The following list shows the positioning algorithms that are part of Loceva. The list is grouped by the research projects that have invented them: - RADAR: Nearest neighbor(s) in signal space, k nearest neighbors in signal space - PlaceLab: K nearest neighbors p unknown, Ranking - Rice: Histogram, Gaussian - Horus: Horus Although the main focus of Loceva is on positioning algorithms, it also contains a few continuous user tracking algorithms: - RADAR: Viterbi-like algorithm - Rice: Tracking - Horus: Horus 4. Analysis After selecting a certain scenario and positioning algorithm, Loceva computes the position error that would have occurred in this setting. The position error is defined as the Euclidean distance between the actual position of the user and the position estimate calculated by the algorithm. At the end of each emulation the average position error is printed, and a graph showing a cumulative distribution function of the position error (as shown in the figure below) is generated. Such a graph can be used to compare the position accuracy of different positioning algorithms by determining the median, 95th percentile and so on. Additionally, Loceva can be enabled to create a file that contains a log of intermediate results computed by the selected positioning algorithm. This log can be used with Locana to analyze the behavior of the positioning algorithm in question.
The files in this directory are a CRAWDAD toolset hosted by IEEE DataPort. About CRAWDAD: the Community Resource for Archiving Wireless Data At Dartmouth is a data resource for the research community interested in wireless networks and mobile computing. CRAWDAD was founded at Dartmouth College in 2004, led by Tristan Henderson, David Kotz, and Chris McDonald. CRAWDAD toolsets are hosted by IEEE DataPort as of November 2022. Note: Please use the tools in an ethical and responsible way with the aim of doing no harm to any person or entity for the benefit of society at large. Please respect the privacy of any human subjects whose wireless-network activity is captured by the tools and comply with all applicable laws, including without limitation such applicable laws pertaining to the protection of personal information, security of data, and data breaches. Please do not apply, adapt or develop algorithms for the extraction of the true identity of users and other information of a personal nature, which might constitute personally identifiable information or protected health information under any such applicable laws. Do not publish or otherwise disclose to any other person or entity any information that constitutes personally identifiable information or protected health information under any such applicable laws derived from the tools through manual or automated techniques. Please acknowledge the source of the tools in any publications or presentations reporting use of this tools. Citation:
Thomas King, Stephan Kopf, Thomas Butter, Hendrik Lemelson, Thomas Haenselmann, Wolfgang Effelsberg, CRAWDAD toolset tools/analyze/location/loceva (v. 2007‑09‑14) https://doi.org/10.15783/C7601W, Sep 2007.
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These datasets are part of Community Resource for Archiving Wireless Data (CRAWDAD). CRAWDAD began in 2004 at Dartmouth College as a place to share wireless network data with the research community. Its purpose was to enable access to data from real networks and real mobile users at a time when collecting such data was challenging and expensive. The archive has continued to grow since its inception, and starting in summer 2022 is being housed on IEEE DataPort.