Enhancing Indoor Localization with Proximity Information in WSN

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.