An admission control algorithm for multi-hop 802.11e-based WLANs
Computer Communications
An intelligent infotainment dissemination scheme for heterogeneous vehicular networks
Expert Systems with Applications: An International Journal
A dynamic system approach for radio location fingerprinting in wireless local area networks
IEEE Transactions on Communications
Sybot: an adaptive and mobile spectrum survey system for wifi networks
Proceedings of the sixteenth annual international conference on Mobile computing and networking
LOVINA: Location-aware Virtual Navigation system based on active RFID
International Journal of Ad Hoc and Ubiquitous Computing
Pervasive and Mobile Computing
Adaptive radio maps for pattern-matching localization via inter-beacon co-calibration
Pervasive and Mobile Computing
Clearing a crowd: context-supported neighbor positioning for people-centric navigation
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Theoretical entropy assessment of fingerprint-based Wi-Fi localization accuracy
Expert Systems with Applications: An International Journal
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In wireless networks, a client's locations can be estimated using the signals received from various 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 However, the radio map can be outdated when the signal-strength values change with time due to environmental dynamics, and repeated data calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as LEMT (Location Estimation using Model Trees), to reconstruct a radio map using real-time signal- strength readings received at the reference points. This algorithm can take into account real-time signal-strength values at each time point and make use of the dependency between the estimated locations and reference points. We show that this technique can effectively accommodate the variations of signal strength over different time periods without the need to rebuild the radio maps repeatedly. We demonstrate the effectiveness of our proposed technique on realistic data sets collected from an 802.11b wireless network and a RFID-based network.