Evaristo J. Abril

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks

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.

Prior NLOS Measurement Correction for Positioning in Cellular Wireless Networks

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.

Dynamic Routing and Wavelength Assignment in Optical Networks by Means of Genetic Algorithms

We propose a novel genetic algorithm for solving the dynamic routing and wavelength assignment (DRWA) problem in wavelength-routed optical networks. The algorithm not only obtains low call blocking probability, but it also employs a very short computation time. Moreover, it is capable of providing fairness among connections, that is, to offer approximately the same quality of service (in terms of blocking probability) for all source-destination node pairs. Since requirements on optical network availability are highly severe, we also propose an extension of the algorithm to provide fault-tolerance capability at the optical layer. It is achieved by means of protection, where each optical connection request is provided with a pair of lightpaths (a primary and a backup lightpath). Again, the genetic algorithm proves to be highly efficient, in this case, at performing routing and wavelength assignment of pairs of lightpaths.

Adaptive Data Fusion for Wireless Localization in Harsh Environments

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 filters 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.

A Cognitive Quality of Transmission Estimator for Core Optical Networks

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.

Hybrid RSS-RTT localization scheme for indoor wireless networks

Nowadays, a variety of information related to the distance between two wireless devices can be easily obtained. This paper presents a hybrid localization scheme that combines received signal strength (RSS) and round-trip time (RTT) information with the aim of improving the previous localization schemes. The hybrid localization scheme is based on an RSS ranging technique that uses RTT ranging estimates as constraints among other heuristic constraints. Once distances have been well estimated, the position of the mobile station (MS) to be located is estimated using a new robust least-squared multilateration (RLSM) technique that combines the RSS and RTT ranging estimates mitigating the negative effect of outliers. The hybrid localization scheme coupled with simulations and measurements demonstrates that it outperforms the conventional RSS-based and RTT-based localization schemes, without using either a tracking technique or a previous calibration stage of the environment.

Topology Assessment Provided by Weighted Barycentric Parameters in Harsh Environment Wireless Location Systems

In wireless location systems deployed in open areas, the statistical distributions of the range estimates are very tractable. However, due to the nature of the wireless propagation in urban and indoor environments, the behavior of the range estimates in such environments is very different. Therefore, the performance assessment results obtained for the systems operating in open areas cannot be transferred to the ones deployed in realistic urban and indoor environments. In this paper, the systematic and random errors (accuracy and precision) and the dilution-of-precision (DOP) in harsh environments are derived for the two most common multilateration algorithms, as well as the performance theoretical benchmark. We show that these quantities are determined by geometric parameters that we call topology-assessment-weighted-barycentric-parameters (TAWBAP), which are the norm of weighted barycenters obtained from the positions of anchors and target. These parameters manage the performance of the multilateration process showing the influence of the geometric configuration in connection with the specific characteristics of each range estimate. The improvement in performance obtained by using the TAWBAP parameters as a network design rule is demonstrated by means of simulations as well as by measurements taken in a real indoor environment. This improvement outperforms at least 25% the one achieved by topology deployments that have been considered as optimal in the literature.

Indoor Location Based on IEEE 802.11 Round-Trip Time Measurements with Two-Step NLOS Mitigation

This paper presents a comprehensive location scheme in a rich multipath environment. It is based on the estimation of the distance between two wireless nodes in line-of-sight (LOS) from the best statistical estimator of the round-trip time (RTT), assuming a linear regression as the model that best relates this statistical estimator to the actual distance. As LOS cannot be guaranteed in an indoor environment, the efiect of non-line-of-sight (NLOS) is mitigated by a two-step correction scheme. At a flrst step, the severe NLOS error is corrected from distance estimates applying the prior NLOS

Implementation of a PID controller for the bandwidth assignment in long-reach PONs

In this paper a proportional-integral-derivative (PID) controller to efficiently allocate bandwidth in high coverage passive optical networks (PONs) is presented. The novelty of this proposal relies on the fact that this is the first proposal to use a PID to control a network parameter in PONs. As the PID takes into account present, past and possible future errors in the bandwidth adjustment, this self-adapting technique results in a very robust process. Simulation results exhibit that not only is it faster and more stable than other algorithms, but also it auto-adapts the resources very efficiently when facing real time changes in the network parameters or in the bandwidth conditions. In fact, the standard deviation of the difference between the allocated and the guaranteed bandwidth is reduced by up to 50% and the convergence speed is up to four times quicker than other proposals.

A NEW METRIC TO ANALYZE PROPAGATION MODELS

Deterministic propagation models are typically validated by performing comparisons between real and simulated E-field envelope distributions. These distributions correspond to straight spatial segments and, occasionally, also surfaces. This approach is correct to study large scale fading for relatively large distances. However, in a real environment and shorter distances, there are too many details to consider. As a result, it is almost impossible to reach a point by point match in a minimally realistic experiment. There are two ways to deal with this problem. The first one is to model every minor detail everywhere around us, keeping the point by point metric. The second one is to change that metric in order to admit, at least in part, that we can not take into account of all the details. If uncertainty can not be eliminated, we should learn to take advantage of it by using a statistical metric like the one proposed here. This paper uses such a kind of metric to validate several structural and geometrical simplifications of a model for the transition between outdoor and indoor propagation that has been recently published. Furthermore, we demonstrate that this metric has helped us to improve and understand better this model, while revealing unexpected model properties at the same time. Corresponding author: J. Blas (juabla@tel.uva.es).