Abstract
Abstract Position information is an important aspect of a mobile device’s context. While GPS is widely used to provide location information, it does not work well indoors. Wi-Fi network infrastructure is found in many public facilities and can be used for indoor positioning. In addition, the ubiquity of Wi-Fi-capable devices makes this approach especially cost-effective. In recent years, “folksonomy”-like systems such as Wikipedia or Delicious Social Bookmarking have achieved huge successes. User collaboration is the defining characteristic of such systems. For indoor positioning mechanisms, it is also possible to incorporate collaboration in order to improve system performance, especially for fingerprinting-based approaches. In this article, a robust and efficient model is devised for integrating human-centric collaborative feedback within a baseline Wi-Fi fingerprinting-based indoor positioning system. Experiments show that the baseline system performance (i.e., positioning error and precision) is improved by collecting both positive and negative feedback from users. Moreover, the feedback model is robust with respect to malicious feedback, quickly self-correcting based on subsequent helpful feedback from users.
Highlights
After over a decade of research and development, location-aware services have gradually penetrated into real life
Location-aware applications have been confined to outdoor environments
In order to carefully explore the benefits of the core contribution of this work, we found it necessary to simplify the problem domain and assumed that the Received Signal Strength (RSS) measurements are stable over time
Summary
After over a decade of research and development, location-aware services have gradually penetrated into real life. Given an RSS live measurement (observation) vector generated at location P as RPs , the resulting likelihood estimate between RPs and fingerprint Fi in system anchor Asi is the sum of n weighted density functions n. Whenever the system receives a user-suggested location associated with its current RSS measurement, denoted as user fingerprint, the system creates a temporary user anchor (Au) If this anchor is sufficiently similar to an existing user anchor in the model, it is merged with it, and the α factor is updated. The α factor increases fastest with the first few instances of the user anchor, becoming stable once a sufficient number of feedback events are received The rationale for this design is to allow the system to quickly adapt to new information provided by the users, but without this feedback overpowering the system. If more and more users reject the same set of anchors, they will not be chosen as the top-k due to the small value of the β factor
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