Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Statistics for Spatial Data.

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.

Locating the nodes: cooperative localization in wireless sensor networks

With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

If a dense network of static wireless sensors is deployed to measure a time-varying isotropic random field, then sensor data itself, rather than range measurements using specialized hardware, can be used to estimate a map of sensor locations. Furthermore, distributed and scalable sensor localization algorithms can be derived. We apply the manifold learning algorithms, Isomap, locally linear embedding (LLE), and Hessian LLE (HLLE). The HLLE-based estimator demonstrates the best bias and variance performance, but may not be robust for all random sensor deployments.

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Manifold learning algorithms for localization in wireless sensor networks

This paper presents novel methodologies which allow robust secret key extraction from radio channel measurements which suffer from real-world non-reciprocities and a priori unknown fading statistics. These methodologies have low computational complexity, automatically adapt to differences in transmitter and receiver hardware, fading distribution and temporal correlations of the fading signal to produce secret keys with uncorrelated bits. Moreover, the introduced method produces secret key bits at a higher rate than has previously been reported. We validate the method using extensive measurements between TelosB wireless sensors.

/pdf/robust-uncorrelated-bit-extraction-methodologies-for-3pbe7gp1ai.pdf

Robust uncorrelated bit extraction methodologies for wireless sensors

Accurate localization of people in indoor and domestic environments is one of the key requirements for ambient assisted living (AAL) systems. This chapter describes how the received signal strength (RSS) measurements collected by a network of static radio transceivers can be used to localize people without requiring them to wear or carry any radio device. We describe a technique named radio tomographic imaging (RTI), which produces real-time images of the change in the radio propagation field of the monitored area caused by the presence of people. People’s locations are inferred from the estimated RTI images. We show results from a long-term deployment in a typical single floor, one bedroom apartment. In order to deal with the dynamic nature of the domestic environment, we introduce methods to make the RTI system self-calibrating. Experimental results show that the average localization error of the system is 0.23 m. Moreover, the system is capable of adapting to the changes in the indoor environment, achieving high localization accuracy over an extended period of time.

/pdf/radio-tomographic-imaging-for-ambient-assisted-living-1ym88a3sg3.pdf

Radio Tomographic Imaging for Ambient Assisted Living

Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This paper introduces a scalable, distributed weighted-multidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors, updates its position estimate by minimizing a local cost function and then passes this update to neighboring sensors. Derived bounds on communication requirements provide insight on the energy e‐ciency of the proposed distributed method versus a centralized approach. For received signal-strength (RSS) based range measurements, we demonstrate via simulation that location estimates are nearly unbiased with variance close to

/pdf/distributed-multidimensional-scaling-with-adaptive-weighting-3x4w6hd3e4.pdf

Distributed Multidimensional Scaling with Adaptive Weighting for Node Localization in Sensor Networks

RSS-based device-free localization (DFL) monitors changes in the received signal strength (RSS) measured by a network of static wireless nodes to locate and track people without requiring them to carry or wear any electronic device. Current models assume that the spatial impact area , i.e., the area in which a person affects a link's RSS, has constant size. This paper shows that the spatial impact area varies considerably for each link. Data from extensive experiments are used to derive a spatial weight model that is a function of the fade level , i.e., a measure of whether a link is experiencing destructive or constructive multipath interference, and of the sign of RSS change. In addition, a measurement model is proposed which calculates for each RSS measurement the probability of a person being located inside the derived spatial impact area. An online radio tomographic imaging (RTI) system is described which uses channel diversity and the presented models. Experiments in an open indoor environment, in a typical one-bedroom apartment and in a through-wall scenario are conducted to determine the performance of the proposed system. We demonstrate that the new system is capable of localizing and tracking a person with high accuracy ( \(\boldsymbol {\leq 0.30}\) m) in all the environments, without the need to change the model parameters.

/pdf/a-fade-level-based-spatial-model-for-radio-tomographic-2c7yrykjeq.pdf

Neal Patwari

Papers

Manifold learning algorithms for localization in wireless sensor networks

Robust uncorrelated bit extraction methodologies for wireless sensors

Radio Tomographic Imaging for Ambient Assisted Living

Distributed Multidimensional Scaling with Adaptive Weighting for Node Localization in Sensor Networks

A Fade Level-Based Spatial Model for Radio Tomographic Imaging