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.

The dynamic and unpredictable characteristics of wireless channels in harsh environments have resulted in a poor performance of localization systems. Conventional implementations rely on unrealistic assumptions driven by tractability requirements, such as linear models or Gaussian errors. In this paper, we present a framework for data fusion in localization systems based on determining likelihood functions that represent the relationship between measurements and distances. In this framework, such likelihoods are dynamically adapted to the propagation conditions. The subsequent usage of a particle filter (PF) leads to an adaptive likelihood particle (ALPA) filter that addresses the nonlinear and non-Gaussian behavior of measurements over time. The ALPA filter's performance is quantified by using received-signal-strength (RSS) and time-of-arrival (TOA) measurements collected with wireless local area network (WLAN) devices. We compare the accuracy obtained to the accuracy of conventional implementations and to the posterior Cramér-Rao lower bound (PCRLB). Both empirical and simulation results show that the proposed ALPA filter significantly improves the accuracy of conventional approaches, obtaining an error close to the PCRLB.

We propose a cognitive Quality of Transmission (QoT) estimator for classifying lightpaths into high or low quality categories in impairment-aware wavelength-routed optical networks. The technique is based on Case-Based Reasoning (CBR), an artificial intelligence technique which solves new problems by exploiting previous experiences, which are stored on a knowledge base. We also show that by including learning and forgetting techniques, the underlying knowledge base can be optimized, thus leading to a significant reduction on the computing time for on-line operation. The performance of the cognitive estimator is evaluated in a long haul and in an ultra-long haul network, and we demonstrate that it achieves more than 98% successful classifications, and that it is up to four orders of magnitude faster when compared with a non-cognitive QoT estimator, the Q-Tool.

A mobile station (MS) location can be estimated from the measurement of the time of arrival of the signals that travel between each base station and the MS. In this scenario, the existence of non-line-of-sight (NLOS) propagation paths has been considered to be the main drawback to achieve high precision in positioning, since NLOS propagation introduces large and unpredictable errors in the time estimates that are obtained from the measurements. In this paper, we propose a new technique, called prior NLOS measurement correction (PNMC), to effectively correct the measurements from NLOS propagation in a previous stage to the positioning process. PNMC is based on a statistical processing of a record of measurements taken over a time window. This processing relies on the statistical estimate of the NLOS measurement ratio present in our record. This estimate is used to range the NLOS recorded measurements into segments. Finally, the correction is carried out by subtracting the expected NLOS errors for each segment. Several simulations have been conducted to show the increase in accuracy obtained by the usage of PNMC and the great improvement that this prior measurement correction means to subsequent wireless location and positioning techniques.