Breathfinding: A Wireless Network that Monitors and Locates Breathing in a Home

This paper explores using RSS measurements on many links in a wireless network to estimate the breathing rate of a person, and the location where the breathing is occurring, in a home, while the person is sitting, laying down, standing, or sleeping. The main challenge in breathing rate estimation is that "motion interference", i.e., movements other than a person's breathing, generally cause larger changes in RSS than inhalation and exhalation. We develop a method to estimate breathing rate despite motion interference, and demonstrate its performance during multiple short (3-7 minute) tests and during a longer 66 minute test. Further, for the same experiments, we show the location of the breathing person can be estimated, to within about 2 m average error in a 56 square meter apartment. Being able to locate a breathing person who is not otherwise moving, without calibration, is important for applications in search and rescue, health care, and security

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Radio Tomographic Imaging using a Modified Maximum Likelihood Estimator for Image Reconstruction in Various Environments

Radio Tomographic Imaging (RTI) is an emerging Device-Free Passive Localization (DFPL) technology. Radio Tomographic Imaging (RTI) involves using a set of small low cost wireless transceivers to create a Wireless Sensor Network (WSN) around an Area of Interest (AoI). Furthermore, the Received Signal Strength (RSS) between transceiver pairs is utilized to reconstruct an image from the signal attenuation caused by an object disrupting the links. This image can then be utilized for multiple applications ranging from localization to target detection and tracking. This enhances the importance of image resolution in order to capture the actual size of the objects as well as the ability to resolve multiple objects in an AoI. The objective of this research is to propose a new image formation technique for a reconstructed image within aWSN. This was accomplished using a modified Maximum Likelihood Estimate (MLE) function that forces the desired solution to be positive. Other regularization techniques must implement different methods to mitigate the undesired singular values caused from a non-invertible matrix. Additionally, the research highlights the performance of the modified MLE estimator and the robustness of improved image resolution in three different environments

Tracking mobile targets through Wireless Sensor Networks

In recent years, advances in signal processing have led to small, low power, inexpensive Wireless Sensor Network (WSN). The signal processing in WSN is different from the traditional wireless networks in two critical aspects: firstly, the signal processing in WSN is performed in a fully distributed manner, unlike in traditional wireless networks; secondly, due to the limited computation capabilities of sensor networks, it is essential to develop an energy and bandwidth efficient signal processing algorithms. Target localisation and tracking problems in WSNs have received considerable attention recently, driven by the necessity to achieve higher localisation accuracy, lower cost, and the smallest form factor. Received Signal Strength (RSS) based localisation techniques are at the forefront of tracking research applications. Since tracking algorithms have been attracting research and development attention recently, prolific literature and a wide range of proposed approaches regarding the topic have emerged. This thesis is devoted to discussing the existing WSN-based localisation and tracking approaches. This thesis includes five studies. The first study leads to the design and implementation of a triangulation-based localisation approach using RSS technique for indoor tracking applications. The presented work achieves low localisation error in complex environments by predicting the environmental characteristics among beacon nodes. The second study concentrates on investigating a fingerprinting localisation method for indoor tracking applications. The proposed approach offers reasonable localisation accuracy while requiring a short period of offline computation time. The third study focuses on designing and implementing a decentralised tracking approach for tracking multiple mobile targets with low resource requirements. Despite the interest in target tracking and localisation issues, there are few systems deployed using ZigBee network standard, and no tracking system has used the full features of the ZigBee network standard. Tracking through the ZigBee is a challenging task when the density of router and end-device nodes is low, due to the limited communication capabilities of end-device nodes. The fourth study focuses on developing and designing a practical ZigBee-based tracking approach. To save energy, different strategies were adopted. The fifth study outlines designing and implementing an energy-efficient approach for tracking applications. This study consists of two main approaches: a data aggregation approach, proposed and implemented in order to reduce the total number of messages transmitted over the network; and a prediction approach, deployed to increase the lifetime of the WSN. For evaluation purposes, two environmental models were used in this thesis: firstly, real experiments, in which the proposed approaches were implemented on real sensor nodes, to test the validity for the proposed approaches; secondly, simulation experiments, in which NS-2 was used to evaluate the power-consumption issues of the two approaches proposed in this thesis

Location Estimation in Wireless Communication Systems

Localization has become a key enabling technology in many emerging wireless applications and services. One of the most challenging problems in wireless localization technologies is that the performance is easily affected by the signal propagation environment. When the direct path between transmitter and receiver is obstructed, the signal measurement error for the localization process will increase significantly. Considering this problem, we first propose a novel algorithm which can automatically detect and remove the obstruction and improve the localization performance in complex environment. Besides the environmental dependency, the accuracy of target location estimation is highly sensitive to the positions of reference nodes. In this thesis, we also study on the reference node placement, and derive an optimum deployment scheme which can provide the best localization accuracy. Another challenge of wireless localization is due to insufficient number of reference nodes available in the target\u27s communication range. In this circumstance, we finally utilize the internal sensors in today\u27s smartphones to provide additional information for localization purpose, and propose a novel algorithm which can combine the location dependent parameters measured from sensors and available reference nodes together. The combined localization algorithm can overcome the error accumulation from sensor with the help of only few number of reference nodes

Performance Limits and Geometric Properties of Array Localization

Location-aware networks are of great importance and interest in both civil and military applications. This paper determines the localization accuracy of an agent, which is equipped with an antenna array and localizes itself using wireless measurements with anchor nodes, in a far-field environment. In view of the Cram\'er-Rao bound, we first derive the localization information for static scenarios and demonstrate that such information is a weighed sum of Fisher information matrices from each anchor-antenna measurement pair. Each matrix can be further decomposed into two parts: a distance part with intensity proportional to the squared baseband effective bandwidth of the transmitted signal and a direction part with intensity associated with the normalized anchor-antenna visual angle. Moreover, in dynamic scenarios, we show that the Doppler shift contributes additional direction information, with intensity determined by the agent velocity and the root mean squared time duration of the transmitted signal. In addition, two measures are proposed to evaluate the localization performance of wireless networks with different anchor-agent and array-antenna geometries, and both formulae and simulations are provided for typical anchor deployments and antenna arrays.Comment: to appear in IEEE Transactions on Information Theor

UWB Localization of people-accuracy aspects

Zaied, Salah: UWB Localization of people-accuracy aspects Zusammenfassung UWB-Sensoren sind durch eine sehr große Bandbreite gekennzeichnet. Diese Bandbreite ermöglicht es, Objekte mit einer sehr guten Genauigkeit zu lokalisieren. Passive Objekte, die kein Sender oder Empfänger tragen, sind mittels zurückgestreuten elektromagnetischen Wellen zu lokalisieren. Es existieren unterschiedliche Lokalisierungsmethoden. Die Masterarbeit analysiert unterschiedliche laufzeitbasierten Lokalisierungsansätze. Die Masterarbeite bietet verschiedene Lösungen von dem Lokalisierungsproblem, der mathematisch mit einem System von quadratischen Gleichungen beschrieben ist. Die Lösungen decken folgende Ansätze ab: Linearisierung mittels einer Taylor Reihe Entwicklung, Kreuzung von Ellipsen and sphärische Interpolation. Die Masterarbeit analysiert Genauigkeit von den Algorithmen in unterschiedlichen Einsatzszenarien. Die Lokalisierungsgenauigkeit war anhand der Hauptkomponentenanalyse ausgewertet.UWB sensors feature very large bandwidth. This bandwidth allows very accurate localization of tag-free targets such as people. In this case, an UWB localization system localizes tag-free by means of backscattered electromagnetic waves. There exist different localization approaches. The thesis concerns with the accuracy aspects of time-of-arrival based localization approaches. The thesis provides different solutions to the localization problem which is mathematically described by a system of two dimensional nonlinear equations of the second order like. These solutions cover: Taylor series linearization, intersection of ellipses and the spherical interpolation. The thesis analyses performance of these localization approaches in different simulation scenarios. The principal component analysis was used to evaluate precision of these localization approaches.Ilmenau, Techn. Univ., Master-Arbeit, 201