Considerations on Quality Metrics for Self-localization Algorithms

Eckert J, Villanueva F, German R, Dressler F (2011)

Publication Type: Conference contribution

Publication year: 2011

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer-verlag

Edited Volumes: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Book Volume: LNCS

Pages Range: 104-115

Conference Proceedings Title: Proceedings of 5th IEEE/IFIP International Workshop on Self-Organizing Systems (IWSOS 2011)

Event location: Karlsruhe, Germany DE

Journal Issue: 6557

URI: http://www7.informatik.uni-erlangen.de/~eckert/publications/inc/eckert2011alf-abstract.shtml

DOI: 10.1007/978-3-642-19167-1_10

Abstract

The demand for location awareness and, therefore, the demand for self-localization techniques is continuously increasing. As a result, a good number of systems and methods for self-localization have been developed. Almost every system described in the literature exploits specific hardware or scenario features to solve the positioning issue, e.g. by using anchor nodes, relying on distances or angles, and even focusing on quite different distances ranging from centimeters to several kilometers. In many cases, the metrics used to evaluate the localization quality have been chosen according to the scenario. In this paper, we thoroughly discuss the most frequently used metrics for evaluating the quality of self-localization techniques. According to our findings, careful handling of some commonly used metrics is strongly required. We further propose an area-based solution that is especially helpful to measure and to compare different localization systems, which only need exact localization in a local context independently from specific scenario or hardware requirements. In this paper, we try to shed light on the question how to compare those very different techniques. In particular, we suggest the use one of two attribute-independent metrics. The first one is a generalization of an already quite popular metric, the (GER), and the latter, the (AR), is a new approach based on the covered area. © 2011 Springer-Verlag.

Authors with CRIS profile

Jürgen Eckert Computer Science 7 (Computer Networks and Communication Systems) Reinhard German Computer Science 7 (Computer Networks and Communication Systems)

Involved external institutions

Universidad de Castilla-La Mancha (UCLM) ES Spain (ES)

How to cite

APA:

Eckert, J., Villanueva, F., German, R., & Dressler, F. (2011). Considerations on Quality Metrics for Self-localization Algorithms. In Proceedings of 5th IEEE/IFIP International Workshop on Self-Organizing Systems (IWSOS 2011) (pp. 104-115). Karlsruhe, Germany, DE: Springer-verlag.

MLA:

Eckert, Jürgen, et al. "Considerations on Quality Metrics for Self-localization Algorithms." Proceedings of the 5th IEEE/IFIP International Workshop on Self-Organizing Systems (IWSOS 2011), Karlsruhe, Germany Springer-verlag, 2011. 104-115.

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