Efficient localization methods for wireless sensor networks based on received signal strength = Metodos de localizacion eficiente para redes de sensores basados en la medida de la potencia de señal recibida

La localización de los nodos de una red ad hoc o una red de sensores inalámbrica es de gran valor y utilidad en el contexto de la Inteligencia Ambiental, ya que posibilita un gran número de aplicaciones en las que se necesita conocer la posición de los nodos que realizan las medidas para interpretar la información correctamente y actuar en consecuencia. Algunas de estas aplicaciones pueden ser la gestión de emergencias, la monitorización del tráfico, la agricultura de precisión, el control domótico, la monitorización de pacientes y equipamiento en hospitales, etc. Pero además, independientemente de la aplicación concreta para la que se despliega la red, el conocimiento de la posición de los nodos posibilita el desarrollo de algoritmos que aprovechan esta información para optimizar algunos aspectos de funcionamiento de la red (como el encaminamiento o la compresión de datos), reduciendo el consumo de energía durante las comunicaciones. Es bien sabido que en los dispositivos inalámbricos la energía es un recurso escaso, ya que la alimentación se realiza mediante pilas o baterías de duración limitada. Por esta razón, es muy importante gestionar eficientemente la energía consumida, ya que de ello dependerá la autonomía de los nodos y, por tanto, de la red. La gran cantidad de estudios que abordan el tema de la eficiencia energética desde distintos puntos de vista (a nivel hardware, optimizando el encaminamiento de los datos, etc.) es un indicativo de la importancia que tiene este asunto en las redes ad hoc y en las redes de sensores. Sin embargo, pocos han sido los trabajos dedicados a estudiar el consumo de energía durante el proceso de localización de los nodos de la red. La localización de los nodos en una red inalámbrica consta normalmente de dos fases diferenciadas. En la primera, los nodos intercambian una serie de mensajes y realizan medidas de algún parámetro de la señal radio que reciben de sus nodos vecinos, como pueden ser el tiempo de llegada o la potencia de señal recibida. Posteriormente, estas medidas se procesan para determinar la posición de los nodos. Evidentemente, durante el proceso de localización de los nodos se consume energía, ya que los nodos deben comunicarse entre sí y, además, realizar algunas operaciones de procesado. Es fundamental, pues, que la localización se realice de manera eficiente, es decir, consumiendo poca energía, de modo que la vida útil de la red se prolongue durante el mayor periodo de tiempo posible. En la presente tesis se contribuye a esta idea de localización con eficiencia energética, mediante la concepción, el diseño, el desarrollo y la evaluación de nuevos métodos y técnicas de localización para redes ad hoc o redes de sensores eficientes en cuanto al consumo de energía y capaces de obtener una determinada precisión en la estimación de las posiciones. La tesis se centra en la localización basada en medidas de potencia de señal recibida, aunque la mayor parte de las técnicas propuestas son igualmente aplicables en otros tipos de localización. En particular, se ha abordado el problema desde los dos niveles que intervienen en el proceso de localización y que contribuyen al consumo energético: a nivel de comunicaciones radio y a nivel de procesado de información. A nivel radio se ha estudiado cómo realizar las comunicaciones entre los nodos de la red de forma eficiente cuando el objetivo es localizar. Es fácil comprender que este problema es diferente al de la transmisión eficiente de información a través de la red, ya que en el caso de la localización el objetivo es conseguir una determinada precisión en las medidas de los parámetros de la señal (la potencia de señal recibida, por ejemplo) que se utilizarán para calcular la posición de los nodos, mientras que en la transmisión de información lo que interesa es recibir correctamente los datos enviados por los distintos nodos. Así pues, se ha diseñado una estrategia de transmisión de mensajes que garantiza una determinada precisión en la medida de los parámetros de la señal radio y que, al mismo tiempo, minimiza la energía consumida durante estas comunicaciones. Desde el punto de vista del procesado de la información, se han estudiado diferentes algoritmos para calcular la posición de los nodos a partir de las medidas realizadas de potencia recibida u otro parámetro radio. Se ha prestado una particular atención a la robustez de los algoritmos frente a posibles errores en modelado del canal de propagación, proponiéndose un conjunto de técnicas destinadas a mejorar la precisión de los resultados en situaciones prácticas en las que el canal de propagación no está perfectamente caracterizado. En general, para una precisión dada de las medidas radio realizadas, los algoritmos de posicionamiento que consiguen una mayor precisión en el resultado de localización son computacionalmente más complejos y, por tanto, implican un mayor consumo energético. Así pues, se ha analizado el compromiso entre eficiencia energética y precisión de distintos algoritmos y se han establecido algunas pautas para la selección del algoritmo en función de los requisitos de la aplicación. Finalmente, para completar esta tesis, las ideas presentadas con anterioridad se han aplicado al caso de sistemas de localización híbrida basados en la medida de la potencia de señal recibida y en la navegación inercial. En particular, se han estudiado diversas estrategias para combinar ambos tipos de localización de forma óptima, es decir, minimizando el consumo de energía y garantizando una determinada precisión objetivo en el resultado conjunto de localización. The localization of the nodes in ad hoc and sensor networks has an enormous value and utility in the context of Ambient Intelligence, since it enables a great number of applications in which the position of the nodes that collect the measurements is needed to correctly interpret the gathered information and act accordingly. Some of these applications could be emergency management, traffic monitoring, precision agriculture, domotic control, in-hospital patient and equipment monitoring, etc. Furthermore, disregarding the specific application for which the network is deployed, knowing the position of the nodes enables the development of algorithms that leverage this information to optimize some functional aspects of the network (such as routing or in-network data compression), reducing the energy consumption during communications. It is well known that energy is a scarce resource in wireless devices, since they are powered by batteries with limited life-time. For this reason, it is very important to manage the energy consumption in an efficient way, as this will impact directly on the autonomy of the nodes and, therefore, of the network. The great number of studies that deal with the topic of energy efficiency from different points of view (hardware optimization, routing optimization, etc.) is an indicative of the significance of this issue for ad hoc and sensor networks. However, little work has focused on studying the energy consumption during the process of localizing the nodes of the network. The localization of the nodes in a wireless network embraces two different steps. First, the nodes exchange a series of messages and perform measurements of some parameters of the radio signals that receive from their neighboring nodes, such as the Time of Arrival or the Received Signal Strength. Then, these measurements are processed in order to calculate the position of the nodes. Clearly, there is some energy consumption during the localization of the nodes, given that the nodes must communicate and perform some processing operations. Therefore, it is essential to perform the localization in an efficient way, that is, consuming a small amount of energy, so that the life of the network can be as long as possible. This thesis contributes to this idea of energy-efficient localization by conceiving, designing, developing and evaluating new localization strategies and algorithms for ad hoc and sensor networks that are efficient regarding the energy consumption and capable of achieving a given accuracy in the position estimation. The thesis is focused on received signal strength -based localization, although most of the proposed techniques are equally applicable to other kinds of localization. In particular, the problem has been tackled from the two different levels that take part in the localization process and contribute to the energy consumption: radio communications and information processing. Regarding the radio issues, we have studied how to perform the communication between the nodes in an efficient manner when the goal of this communication is the localization. It is easy to understand that this problem differs from the efficient transmission of information through the network, since in the localization case the objective is to achieve a given accuracy in the measurement of the signal parameters (the received signal strength, for instance), which will be used to calculate the position of the nodes, whereas in information transmission the interest relies on correctly receiving the transmitted data. Therefore, we have designed a strategy for transmitting localization packets that is able to achieve a required accuracy in the measurements of the radio parameters and, at the same time, minimizes the total energy consumption due to the communication of these localization packets. From the point of view of information processing, different algorithms have been studied to calculate the position of the nodes of the network using the received signal strength measurements or other measurements. A particular attention has been paid to the robustness of these algorithms to possible errors introduced by the modeling of the propagation channel. We have proposed a collection of techniques aimed at improving the accuracy of the localization results in practical situations in which the propagation channel is not perfectly characterized. As a general rule, for a given accuracy in the collected radio measurements, positioning algorithms that achieve a better accuracy in the localization result have a higher computational complexity and, as a result, a higher energy consumption. Therefore, the trade-off between energy efficiency and accuracy of the different algorithms has been analyzed and some guidelines for the selection of the more suitable algorithm depending on the application requirements have been given. Finally, to complete this thesis, the ideas presented before have been applied to the case of hybrid localization systems based on the combination of received signal strength measurements with inertial navigation. In particular, we have studied several strategies aimed at combining these two localization techniques in an optimal manner, that is, minimizing the energy consumption and guaranteeing a given accuracy in the combined localization result

Enhancing the Performance of Propagation Model-Based Positioning Algorithms

Object localization in wireless networks through Received Signal Strength (RSS) measurements requires a precise estimation of the signal attenuation model in order to produce meaningful results. The popular lognormal channel model, widely adopted to describe the signal strength attenuation as a function of the distance between nodes, turns out to be too simplistic when applied to a real scenario. In this paper, we analyze two possible improvements to this model: on one hand, we build a different channel model for each reference node in the network, with the aim of tackling the anisotropy of the environment. On the other hand, we explicitly append to the lognormal model a term to account for walls attenuation. A thorough experimental testbed demonstrates the potentials of the two approaches, with the second one being especially useful to counteract the effect of the limited sensitivity of practical wireless receivers

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'

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 model's 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 algorithm's simplicity and its low computational and memory costs make it adequate to be used in real systems

Trajectory Reconstruction Techniques for Evaluation of ATC Systems

This paper is focused on trajectory reconstruction techniques for evaluating ATC systems, using real data of recorded opportunity traffic. We analyze different alternatives for this problem, from traditional interpolation approaches based on curve fitting to our proposed schemes based on modeling regular motion patterns with optimal smoothers. The extraction of trajectory features such as motion type (or mode of flight), maneuvers profile, geometric parameters, etc., allows a more accurate computation of the curve and the detailed evaluation of the data processors used in the ATC centre. Different alternatives will be compared with some performance results obtained with simulated and real data sets

Comparison of Localization Methods Using Calibrated and Simulated Fingerprints for Indoor Systems Based on Bluetooth and WLAN Technologies

This paper compares two different localization algorithms to face the problem of indoor positioning using Bluetooth and WLAN technologies, which we have called: the fusion algorithm and the combination algorithm. The first algorithm is based on the construction of a fusion map using WiFi and Bluetooth power values. Considering the three lowest values of a defined distance, we compute the coordinates of the target point that we want to localize. In the second algorithm, the location determination is carried out independently with every single technology; then, results are combined to obtain a final estimated position. The performance of these methods has been tested experimentally using a simulated map and a real calibrated one. Using a real calibrated map, the localization errors obtained with the fusion algorithm are smaller than with the combination one, while when using a simulated map there is almost no difference between both algorithms. The results of the experiments made with the real calibrated map are a little better than using the simulated map, but the improvement obtained using the real map is not enough to confirm that using this one is worth, because of the effort necessary to build it

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 Crossbow's 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

Dynamic chanel model LMS updating for RSS-based localization

Received signal strength-based localization systems usually rely on a calibration process that aims at characterizing the propagation channel. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. This paper proposes a dynamic calibration method to initially calibrate and subsequently update the parameters of the propagation channel model using a Least Mean Squares approach. The method assumes that each anchor node in the localization infrastructure is characterized by its own propagation channel model. In practice, a set of sniffers is used to collect RSS samples, which will be used to automatically calibrate each channel model by iteratively minimizing the positioning error. The proposed method is validated through numerical simulation, showing that the positioning error of the mobile nodes is effectively reduced. Furthermore, the method has a very low computational cost; therefore it can be used in real-time operation for wireless resource-constrained nodes

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

Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information