EvaLoc: Evaluating Performance Degradation in Wireless Fingerprint-based Indoor Localization
Version: 1,
Uploaded by: Administrator,
Date Uploaded:
25 November 2022
Warning
You are about to be redirected to a website not operated by the Mauritius Research and Innovation Council. Kindly note that we are not responsible for the availability or content of the linked site. Are you sure you want to leave this page?
Many WiFi fingerprint-based indoor localization approaches have been proposed to ease deployment and minimize infrastructure requirement. While researchers have devoted extensive efforts to improving the accuracy of these approaches, the user experience of such deployments in practice is typically far below expectation. One reason that contributes to such discrepancy is that while researchers often evaluate their systems in stable and "benign" environments, the actual environments can be much more dynamic and noisy. In this paper, we address this issue in the following manner. First, we identify factors that can result in significant degradation of localization performance and explore how these factors can be modeled in the localization process. Next, we design a system, EvaLoc, that takes fingerprinting data collected as input and provides accuracy prediction on the localization performance under different conditions. Our evaluation in 15 different locations covering around 25000 m2 shows that EvaLoc is able to produce localization result that better matches the user experience.
