Paula Tarrío

Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling.

A new positioning technique for RSS-Based localization based on a weighted least squares estimator

In this paper we propose the use of a weighted least squares estimator to calculate the position of a mobile node in RSS-based localization systems for ad hoc networks. This technique outperforms the traditional positioning algorithms in terms of localization accuracy and robustness to inaccuracy in the channel model. The performance of the method is shown both through numerical simulations and through some experiments with real data for a wireless sensor network and a WiFi network.

Analysis of key aspects to manage wireless sensor networks in ambient assisted living environments

Wireless Sensor Networks (WSN) based on ZigBee/IEEE 802.15.4 will be key enablers of non-invasive, highly sensitive infrastructures to support the provision of future ambient assisted living services. This paper addresses the main design concerns and requirements when conceiving ambient care systems (ACS), frameworks to provide remote monitoring, emergency detection, activity logging and personal notifications dispatching services. In particular, the paper describes the design of an ACS built on top of a WSN composed of Crossbows MICAz devices, external sensors and PDAs enabled with ZigBee technology. The middleware is integrated in an OSGi framework that processes the acquired information to provide ambient services and also enables smart network control. From our experience, we consider that in a future, the combination of ZigBee technology together with a service oriented architecture may be a versatile approach to AAL services offering, both from the technical and business points of view.

LAURA — LocAlization and Ubiquitous monitoRing of pAtients for health care support

This works illustrates the LAURA system, which performs localization, tracking and monitoring of patients hosted at nursing institutes by exploiting a wireless sensor network based on the IEEE 801.15.4 (Zigbee) standard. We focus on the indoor personal localization module, which leverages a method based on received signal strength measurements, together with a particle filter to perform tracking of moving patients. We discuss the implementation and dimensioning of the localization and tracking system using commercial hardware, and we test the LAURA system in real environment, both with static and moving patients, achieving an average localization error lower than 2 m in 80% of the cases. The data sets containing the real measurements of received signal strengths collected during the experiments are made publicly available to enable reproducible research.

Real time calibration for RSS indoor positioning systems

Due to the random characteristics of the indoor propagation channel, received signal strength-based localization systems usually need to be manually calibrated once and again to guarantee their best performance. Calibration processes are costly in terms of time and resources, so they should be eliminated or reduced to a minimum. In this direction, this paper presents an optimization algorithm to automatically calibrate a propagation channel model by using a Least Mean Squares technique: RSS samples gathered in a number of reference points (with known positions) are used by a LMS algorithm to calculate those values for the channel models constants that minimize the error computed by a hyperbolic triangulation positioning algorithm. Preliminary results on simulated and real data show that the localization error in distance is effectively reduced after a number of training samples. The LMS algorithms simplicity and its low computational and memory costs make it adequate to be used in real systems.

An Indoor Location Method Based on a Fusion Map Using Bluetooth and WLAN Technologies

This paper proposes a method for merging Bluetooth and WLAN technologies to face the problem of indoor positioning. The method consists in the construction of a fusion map based on calibrated WiFi RSS and simulated Bluetooth RSSI. On a recent work, we have presented a different approach for fusing both technologies. The performance of this method is tested experimentally and the comparison between the localization results obtained using both technologies and using only WiFi is presented.

CASanDRA: A Framework to Provide Context Acquisition Services ANd Reasoning Algorithms for Ambient Intelligence Applications

The development of ambient intelligence (AmI) applications usually implies dealing with complex sensor access and context reasoning tasks, which may significantly slow down the application development cycle when vertically assumed. To face this issue, we present CASanDRA, a middleware which provides easily consumable context information about a given user and his environment, retrieving and fusing data from personal mobile devices and external sensors. The framework is built following a layered service oriented approach. The output data from every CASanDRA’s layer are fully accessible through semantic interfaces; this allows AmI applications to retrieve raw context features, aggregated context data and complex ‘ images of context’, depending on their information needs. Moreover, different query modes -subscription, event-based, continuous and on-demand- are available. The current ‘mobile-assisted’ version of CASanDRA is composed by a CASanDRA Server, developed on an applications container and hosting the system intelligence, and CASanDRA Lite, a mobile client bundling a set of sensor level acquisition services. How an AmI application may be effortlessly built on CASanDRA is described in the paper through the design of an ‘ Ambient Home Care Monitor’.

Analysis of tracking methods for wireless indoor localization

In this paper we perform a comparative analysis of several localization and tracking methods based on WIFI networks. We describe the signal environment basis of the position observations, and we discuss on training needs, localization computational needs, accuracy, stability, etc. The study is based on simulations of the signal fading effects, and on measurements taken in an experimental deployment

An RSS Localization Method Based on Parametric Channel Models

In this paper a new localization method based on parametric channel models is presented. In order to reduce localization errors when working with real data, we also propose to calculate the median value of several received signal strength measurements prior to the application of the localization method. The performance of the method is shown through both numerical simulations and some tests with real data from a wireless sensor network.

A data fusion framework for context-aware mobile services

The analysis of some context-aware services, in which the central element comes to be the user and its mobile device plunged in a rich and heterogeneous sensing environment, has driven us to face the design of context-aware systems as a multisensor data fusion process. In this paper we propose a fusion framework that describes the information flows and identifies the necessary functional blocks to build context-aware systems whose aim is to accomplish a collaborative dasiamissionpsila. Our three-staged model is inspired in the JDL fusion model, reformulated to fulfil context-aware systems functional modelling needs. It is focused on supporting the process of building and analyzing the systempsilas dasiainformational picturepsila, to later reason about it and make decisions on services, infrastructure and user interaction. In contrast to traditional fusion models, our proposal underlines the non-linearity of context-aware problems solving.