  Wireless indoor localization has attracted significant amount of attention in
recent years. Using received signal strength (RSS) obtained from WiFi access
points (APs) for establishing fingerprinting database is a widely utilized
method in indoor localization. However, the time-variant problem for indoor
positioning systems is not well-investigated in existing literature. Compared
to conventional static fingerprinting, the dynamicallyreconstructed database
can adapt to a highly-changing environment, which achieves sustainability of
localization accuracy. To deal with the time-varying issue, we propose a
skeleton-assisted learning-based clustering localization (SALC) system,
including RSS-oriented map-assisted clustering (ROMAC), cluster-based online
database establishment (CODE), and cluster-scaled location estimation (CsLE).
The SALC scheme jointly considers similarities from the skeleton-based shortest
path (SSP) and the time-varying RSS measurements across the reference points
(RPs). ROMAC clusters RPs into different feature sets and therefore selects
suitable monitor points (MPs) for enhancing location estimation. Moreover, the
CODE algorithm aims for establishing adaptive fingerprint database to alleviate
the timevarying problem. Finally, CsLE is adopted to acquire the target
position by leveraging the benefits of clustering information and estimated
signal variations in order to rescale the weights fromweighted k-nearest
neighbors (WkNN) method. Both simulation and experimental results demonstrate
that the proposed SALC system can effectively reconstruct the fingerprint
database with an enhanced location estimation accuracy, which outperforms the
other existing schemes in the open literature.
