A Probabilistic Approach to WLAN User Location Estimation

The ability to estimate the location of wireless device users has led to many applications that use location information to provide users and service providers with commercially valuable real time information. In this paper, we propose a low-complexity method for location estimation in an indoor wireless local area network (WLAN) deployment. The proposed method relies on measurements of signal strength (SS) of multiple access-points (APs) in a WLAN and is based on a piecewise-linear model that represents distances from APs in terms of SS measurements. The proposed scheme has significantly lower computational and storage requirements as compared to conventional location estimation methods.

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

Nowadays, Wi-Fi fingerprinting-based positioning systems provide enterprises the ability to track their various resources more efficiently and effectively. The main idea behind fingerprinting is to build signal strength database of target area prior to location estimation. This process is called calibration and the positioning accuracy highly depends on calibration intensity. Unfortunately, calibration procedure requires huge amount of time and effort, and makes large scale deployments of Wi-Fi based indoor positioning systems non-trivial. In this research we present a novel location estimation algorithm for Wi-Fi based indoor positioning systems. The proposed algorithm combines signal sampling and hyperbolic location estimation techniques to estimate the location of mobile users. The algorithm achieves cost-efficiency by reducing the number of fingerprint measurements while providing reliable location accuracy. Moreover, it does not require any additional hardware upgrades to the existing network infrastructure. Experimental results show that the proposed algorithm with easy-to-build signal strength database performs more accurate than conventional signal strength-based methods.

The location estimation techniques based on 802.11 b/g wireless local area network (WLAN) have received significant attention in recent years. In this paper we report Cramer -Rao Lower bounds (CRLB) for WLAN user location estimation in a computer laboratory. The CRLBs are computed by using real time radio signal strength measurements performed by user's mobile terminal. This distance error lower bound provides the reference for the accuracy of node localization. The numerical results shows that the mean location error is 1.30 meters and is better as compared to the localization technique using linear least square estimate. The experimental results also demonstrate that the location accuracy improves for higher values of path loss exponent.

Natural ventilation is a good measure to reduce the thermal load of a building and create a good thermal environment for the occupants. To predict the ventilation performance of a building, related ventilation parameters should be estimated. However, obtaining an accurate prediction or estimation before the construction is completed is difficult because of the fluctuating characteristics of turbulent, complex local flow field affected by surrounding buildings and city-scale topology. These problems make the application of natural ventilation difficult, and therefore, an in situ experimental method adopted after the construction of a building is completed would be a more reliable way of obtaining accurate ventilation parameters. The tracer gas method is a frequently used in situ method to examine ventilation performance. However, deterministic evaluation using this method has its drawbacks in terms of accuracy, and the uncertainty of results could be large because of fluctuating environmental conditions and intrinsic error of measurement. Therefore, when parameters related to natural ventilation are estimated, the estimates and their uncertainty should be concurrently evaluated. In this study, using a Bayesian approach, we propose a new estimation method that yields not only the estimates, but also the probability distribution of the estimates. To validate the proposed method, we reproduced a tracer gas experiment using computational fluid dynamics (CFD) simulation because the experimental absolute true values that are unknown in most situations were required for evaluating the proposed method. Using the data from CFD simulation, estimations of the ventilation rate and effective room volume were obtained using two different methods: probabilistic estimation based on Bayes' theorem, and deterministic estimation using the quasi-Newton method. In the probabilistic estimation, a method of simultaneously estimating the degree of the difference between the measurement value and the prediction value required for estimation was proposed. The estimation results showed that the estimated values of the deterministic method and probabilistic method (mode of probability distribution function) were very close to the true value set in the CFD simulation. However, the probabilistic method has some advantages over the deterministic method in that the former method can provide not only the estimates, but also the probability distribution of each estimate and its confidence interval. Additionally, in the case of a simultaneous estimation of more than two unknown parameters, it is important to analyze the correlation among parameters. By creating a joint probability density function, the correlation between the estimation parameters is determined, and this is another important advantage of the probabilistic estimation method.

Wireless local area networks (WLAN) localization based on received signal strength is becoming an important enabler of location based services. Limited efficiency and accuracy are disadvantages to the deterministic location estimation techniques. The probabilistic techniques show their good accuracy but cost more computation overhead. A Gaussian mixture model based on clustering technique was presented to improve location determination efficiency. The proposed clustering algorithm reduces the number of candidate locations from the whole area to a cluster. Within a cluster, an improved nearest neighbor algorithm was used to estimate user location using signal strength from more access points. Experiments show that the location estimation time is greatly decreased while high accuracy can still be achieved.

Due to the bias of range measurements in non-line-of-sight (NLOS) environments, location schemes performances undergo a dramatical degradation. This paper proposes a set of methods that integrate all the available heterogeneous measurements, without identifying NLOS from line-of-sight (LOS) measurements, to achieve more accurate location estimation. The proposed hybrid schemes combine Radio Signal Strength (RSS) at the three best nodes, in terms of higher RSS measurements, and an angular beam around the angle of arrival (AOA) at the serving BS to give location estimation of the mobile station (MS). The proposed schemes mitigate the NLOS effect in two steps: in the first step, the weighted sum of the intersections between the three best RSS measures weighted circles and the AOA angular beam is derived. In the second step, a weighting location method that utilizes the geometrical feature of cell layout and the three selected RSS range measurements is applied. Simulation results show that the proposed methods can achieve better accuracy compared to the conventional schemes in [1].

This paper describes research towards a system for locating wireless nodes in a home environment requiring merely a single access point. The only sensor reading used for the location estimation is the received signal strength indication (RSSI) as given by an RF interface, e.g., Wi-Fi. Wireless signal strength maps for the positioning filter are obtained by a two-step parametric and measurement driven ray-tracing approach to account for absorption and reflection characteristics of various obstacles. Location estimates are then computed using Bayesian filtering on sample sets derived by Monte Carlo sampling. We outline the research leading to the system and provide location performance metrics using trace-driven simulations and real-life experiments. Our results and real-life walk-troughs indicate that RSSI readings from a single access point in an indoor environment are sufficient to derive good location estimates of users with sub-room precision.

In wireless sensor networks, location estimation of sensor nodes is important. Therefore some location estimation methods have been proposed. Especially, in wireless sensor networks based on IEEE802.15.4, a method using received signal strength indicator (RSSI) which can be measured by each sensor node is one of the most practicable methods in the way that new hardware is not needed and it is suitable also for the miniaturization. However this technique has a weak point that it needs to investigate the propagation environment of the sensor field to estimate the location of sensor nodes in advance. This paper proposes the method that the decision of the parameter of the propagation environment using only measurements obtained during localization without investigating prior. Evaluation of this method is shown by simulation and experiment that it is able to decide the propagation parameter of an extent that doesnpsilat influence the location estimation accuracy without investigating the propagation environment of the sensor field.

Highly dynamic real-time microblog systems have already published petabytes of real-time human sensor data in the form of status updates. However, the lack of user adoption of geo-based features per user or per post signals that the promise of microblog services as location-based sensing systems may have only limited reach and impact. Thus, in this article, we propose and evaluate a probabilistic framework for estimating a microblog user's location based purely on the content of the user's posts. Our framework can overcome the sparsity of geo-enabled features in these services and bring augmented scope and breadth to emerging location-based personalized information services. Three of the key features of the proposed approach are: (i) its reliance purely on publicly available content; (ii) a classification component for automatically identifying words in posts with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. On average we find that the location estimates converge quickly, placing 51% of users within 100 miles of their actual location.

Landscape-aware location-privacy protection in location-based services

Recent years have witnessed the tremendous development of social media, which attracts a vast number of Internet users. The high-dimension content generated by these users provides an unique opportunity to understand their behavior deeply. As one of the most fundamental topics, location estimation attracts more and more research efforts. Different from the previous literature, we find that user's location is strongly related to user interest. Based on this, we first build a detection model to mine user interest from short text. We then establish the mapping between location function and user interest before presenting an efficient framework to predict the user's location with convincing fidelity. Thorough evaluations and comparisons on an authentic data set show that our proposed model significantly outperforms the state-of-the-arts approaches. Moreover, the high efficiency of our model also guarantees its applicability in real-world scenarios.

In wireless sensor networks, location estimation of sensor nodes is important. Therefore some location estimation methods have been proposed. Especially, in wireless sensor networks based on IEEE802.15.4, a method using received signal strength indicator (RSSI) which can be measured by each sensor node is one of the most practicable methods in the way that new hardware is not needed and it is suitable also for the miniaturization. However this technique has a weak point that it needs to investigate the propagation environment of the sensor field to estimate the location of sensor nodes in advance. This paper proposes the method that the decision of the parameter of the propagation environment using only measurements obtained during localization without investigating prior. Evaluation of this method is shown by simulation that it is able to decide the propagation parameter of an extent that doesn't influence the location estimation accuracy without investigating the propagation environment of the sensor field.

In indoor location environment, shadowing, multipath and reflection phenomena make the position estimation inaccurate. Large number of Access Points (APs) equipped with single antenna could obtain enough Receive Signal Strength (RSS) information to describe signal characteristics of the target space. However, it results in a large investment costs. This paper provides a novel indoor positioning solution using the multi-antennas architecture to increase the accuracy of location estimation. The system obtains enough RSS to compute the user location and provides a reasonable cost of deployments in indoor location environment. Experimental and numerical results show that the proposed indoor location estimation demonstrates a high accuracy of positioning and outstanding performance when RSS is collected by smart antenna technique. More specifically, the accuracy is increased from 1.34% to 92.4% and the error variance is reduced by 96% as compared to the traditional location fingerprint method.

In this paper, we propose a novel method for location estimation of smart devices considering a generic shadowed $\alpha-\kappa-\mu$ distribution based $\alpha$-KMS fading environment, which is not considered for localization hitherto. Most of the existing path loss-based methods utilize a standard log-normal model only for localization; however, fading effects need to be considered to appropriately model the Received Signal Strength (RSS) values. Some of the localization methods utilize standard fading models such as Rayleigh, Nakagami-m, and Rician, to name a few; however, such assumptions lead to erroneous location estimates. The generic location estimator is applicable for all environments and provides accurate location estimates with correct estimates of $\alpha-\kappa-\mu$. We propose a feedback-induced gradient ascent algorithm based on feedback distance that maximizes the derived log-likelihood estimate of the actual location. The proposed method also addresses the non-convex nature of the maximum likelihood estimator and is computationally efficient. The performance is evaluated on a simulated testbed, and the localization results outperform existing state-of-the-art methods.