Abstract
This paper focuses on resource-aware and cost-effective indoor-localization at room-level using RFID technology. In addition to the tracking information of people wearing active RFID tags, we also include information about their proximity contacts. We present an evaluation using real-world data collected during a conference: We complement state-of-the-art machine learning approaches with strategies utilizing the proximity data in order to improve a core localization technique further.
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Keywords
- Support Vector Machine
- Global Position System
- Receive Signal Strength
- Proximity Data
- Receive Signal Strength
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Scholz, C., Doerfel, S., Atzmueller, M., Hotho, A., Stumme, G. (2011). Resource-Aware On-line RFID Localization Using Proximity Data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science(), vol 6913. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23808-6_9
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DOI: https://doi.org/10.1007/978-3-642-23808-6_9
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