Enhancing Indoor Localization with Proximity Information in WSN - Osterloh, Martin
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Several studies have pointed out that RSS-based localization methods for indoor environments are inaccurate and faulty. In my work, I hypothesize that RSS-based localization can be enhanced by utilizing proximity information. Instead of considering solely the radio-signals of the item of interest, we also consider the signals of nearby items to enhance localization. Therefore, a information fusion algorithn was developed. In order to test my hypothesis, I developed an infrastructure to collect data from sensors equipped with a CC2420 radio interface.

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Produktbeschreibung
Several studies have pointed out that RSS-based localization methods for indoor environments are inaccurate and faulty. In my work, I hypothesize that RSS-based localization can be enhanced by utilizing proximity information. Instead of considering solely the radio-signals of the item of interest, we also consider the signals of nearby items to enhance localization. Therefore, a information fusion algorithn was developed. In order to test my hypothesis, I developed an infrastructure to collect data from sensors equipped with a CC2420 radio interface.
Autorenporträt
Martin Osterloh was born in 1985 in Muehlhausen, Germany. He received his Diploma (M. Sc.) in Computer Science in October 2010 from Ilmenau, University of Technology. His main interests are wireless sensor networks and their arising challenges. He is working since 2009 for the Digital Enterprise Research Institute in Galway, Ireland.