Conference Paper

Seamless indoor/outdoor location cognition with confidence in wireless systems

DOI: 10.1109/LCN.2010.5735789 Conference: Local Computer Networks (LCN), 2010 IEEE 35th Conference on
Source: IEEE Xplore

ABSTRACT This paper proposes a method of real-time seamless location cognition in WLAN systems based on monitoring, learning and recognizing the statistics of received data traffic. It uses the property that statistics such as average and variance of throughput are often correlated with the location. The method also uses a confidence parameter to quantify the confidence in the location identification, based on comparing matches with multiple candidates. The proposed method can support seamless location cognition by WLAN terminals, indoor and outdoor, without depending on any infrastructure service.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Recently, indoor positioning systems (IPSs) have been designed to provide location information of persons and devices. The position information enables location-based protocols for user applications. Personal networks (PNs) are designed to meet the users' needs and interconnect users' devices equipped with different communications technologies in various places to form one network. Location-aware services need to be developed in PNs to offer flexible and adaptive personal services and improve the quality of lives. This paper gives a comprehensive survey of numerous IPSs, which include both commercial products and research-oriented solutions. Evaluation criteria are proposed for assessing these systems, namely security and privacy, cost, performance, robustness, complexity, user preferences, commercial availability, and limitations.We compare the existing IPSs and outline the trade-offs among these systems from the viewpoint of a user in a PN.
    IEEE Communications Surveys &amp Tutorials 01/2009; · 6.49 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.
    IEEE Journal of Selected Topics in Signal Processing 11/2009; · 3.63 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The term ldquolocation fingerprintingrdquo covers a wide variety of methods for determining receiver position using databases of radio signal strength measurements from different sources. In this work we present a survey of location fingerprinting methods, including deterministic and probabilistic methods for static estimation, as well as filtering methods based on Bayesian filter and Kalman filter. We present a unified mathematical formulation of radio map database and location estimation, point out the equivalence of some methods from the literature, and present some new variants. A set of tests in an indoor positioning scenario using WLAN signal strengths is performed to determine the influence of different calibration and location method parameters. In the tests, the probabilistic method with the kernel function approximation of signal strength histograms was the best static positioning method. Moreover, all filters improved the results significantly over the static methods.
    Positioning, Navigation and Communication, 2009. WPNC 2009. 6th Workshop on; 04/2009