Title:
KNN-complexity-reduced-method
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Description: In wireless networks, a client’s locations can be estimated using signal strength received from signal transmitters. Static
fingerprint-based techniques are commonly used for location estimation, in which a radio map is built by calibrating signal-strength
values in the offline phase. These values, compiled into deterministic or probabilistic models, are used for online localization. However,
the radio map can be outdated when signal-strength values change over time due to environmental dynamics, and repeated data
calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as Location Estimation using Model Trees
(LEMT), to reconstruct a radio map by using real-time signal-strength readings received at the reference points. This algorithm can
take real-time signal-strength values at each time point into account and make use of the dependency between the estimated locations
and reference points. We show that this technique can effectively accommodat
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KNN complexity reduced method.pdf
