A Real-Time Laboratory Testbed For Evaluating Localization Performance Of WIFI RFID Technologies

A realistic comparative performance evaluation of indoor Geolocation systems is a complex and challenging problem facing the research community. This is due to the fact that performance of these systems depends on the statistical variations of the fading multipath characteristics of the wireless channel, the density and distribution of the access points in the area, and the number of the training points used by the positioning algorithm. This problem, in particular, becomes more challenging when we address RFID devices, because the RFID tags and the positioning algorithm are implemented in two separate devices. In this thesis, we have designed and implemented a testbed for comparative performance evaluation of RFID localization systems in a controlled and repeatable laboratory environment. The testbed consists of a real-time RF channel simulator, several WiFi 802.11 access points, commercial RFID tags, and a laptop loaded with the positioning algorithm and its associated user interface. In the real-time channel simulator the fading multipath characteristics of the wireless channel between the access points and the RFID tags is modeled by a modified site-specific IEEE 802.11 channel model which combines this model with the correlation model of shadow fading existing in the literature. The testbed is first used to compare the performance of the modified IEEE 802.11 channel model and the Ray Tracing channel model previously reported in the literature. Then, the testbed with the new channel model is used for comparative performance evaluation of two different WiFi RFID devices

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

A Hardware Platform for Communication and Localization Performance Evaluation of Devices inside the Human Body

Body area networks (BAN) is a technology gaining widespread attention for application in medical examination, monitoring and emergency therapy. The basic concept of BAN is monitoring a set of sensors on or inside the human body which enable transfer of vital parameters between the patient´s location and the physician in charge. As body area network has certain characteristics, which impose new demands on performance evaluation of systems for wireless access and localization for medical sensors. However, real-time performance evaluation and localization in wireless body area networks is extremely challenging due to the unfeasibility of experimenting with actual devices inside the human body. Thus, we see a need for a real-time hardware platform, and this thesis addressed this need. In this thesis, we introduced a unique hardware platform for performance evaluation of body area wireless access and in-body localization. This hardware platform utilizes a wideband multipath channel simulator, the Elektrobit PROPSimâ„¢ C8, and a typical medical implantable device, the Zarlink ZL70101 Advanced Development Kit. For simulation of BAN channels, we adopt the channel model defined for the Medical Implant Communication Service (MICS) band. Packet Reception Rate (PRR) is analyzed as the criteria to evaluate the performance of wireless access. Several body area propagation scenarios simulated using this hardware platform are validated, compared and analyzed. We show that among three modulations, two forms of 2FSK and 4FSK. The one with lowest raw data rate achieves best PRR, in other word, best wireless access performance. We also show that the channel model inside the human body predicts better wireless access performance than through the human body. For in-body localization, we focus on a Received Signal Strength (RSS) based localization algorithm. An improved maximum likelihood algorithm is introduced and applied. A number of points along the propagation path in the small intestine are studied and compared. Localization error is analyzed for different sensor positions. We also compared our error result with the Cramèr- Rao lower bound (CRLB), shows that our localization algorithm has acceptable performance. We evaluate multiple medical sensors as device under test with our hardware platform, yielding satisfactory localization performance

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

Indoor Localization Using Channel State Information with Regression Artificial Neural Network

RÉSUMÉ Dans cette recherche, les informations sur l'état du canal (CSI) sont utilisées pour localiser les stations mobiles dans un environnement intérieur. À cette fin, deux ordinateurs portables équipés de la carte Intel Wireless Wi-Fi Wireless Link 5300 disponible dans le commerce sont utilisés. Les informations CSI sont collectées en établissant une connexion sans fil entre deux machines de plus de 200, 70 et 52 points de référence (RP) aux sixième, cinquième et troisième étages respectivement, dans l’immeuble Lassonde de Polytechnique Montréal servant de banc d’essai expérimental. Différentes approches de localisation sont étudiées et comparées les unes aux autres en termes de précision de localisation. Dans la première approche, les CSI collectés alimentent directement le réseau de neurones artificiels (RNA) en tant que caractéristiques d’entrée et le RNA appris est utilisé en tant qu’algorithme de correspondance du modèle afin de prédire la position de l’utilisateur. La deuxième approche consiste à appliquer à l’entrée de RNA les paramètres pertinents du canal extrait représentant le nombre réduit d’entités à l’entrée de RNA. Enfin, une exploration est effectuée pour trouver la meilleure configuration de couches cachées et de facteurs d'étalement pour les réseaux Perceptron multicouche (MLP) et Réseaux de neurones à régression générale (GRNN), respectivement.----------ABSTRACT In this research, the Channel State Information (CSI) is leveraged to locate mobile stations in an indoor environment. For this purpose, two laptops equipped with the off-the-shelf Intel Wi-Fi Wireless Link 5300 (NIC card) are used. CSI information is collected by establishing a wireless connection between two machines over 200, 70 and 52 reference points (RP) on sixth, fifth, and third floors respectively, in Lassonde building of Polytechnique Montreal as the experimental testbed. Different geolocation approaches are investigated and compared with each other in terms of location accuracy and precision. In the first approach, the collected CSIs are directly fed to the artificial neural network (ANN) as input features and the learned ANN is used as the patternmatching algorithm in order to predict the user’s location. The second approach consists in applying at the input of the ANN the extracted channel relevant parameters representing the reduced number of features at the input of ANN. Finally, exploration is performed to find the best configuration of hidden layers and spread factors for Multilayer Perceptron (MLPs) and General Regression Neural Networks (GRNNs), respectively

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

D21.3 Analysis of initial results at EuWIN@CTTC

Deliverable D21.3 del projecte europeu NEWCOM#The nature of this Deliverable of WP2.1 (“Radio interfaces for next-generation wireless systems”) is mainly descriptive and its purpose is to provide a report on the status of the different Joint Research Activities (JRAs) currently ongoing, some of them being performed on the facilities that are available at EuWInPeer ReviewedPreprin

Towards a Recommender System for In-Vehicle Antenna Placement in Harsh Propagation Environments

This paper presents a novel approach to improving wireless communications in harsh propagation environments to achieve higher overall reliability and durability of wireless battery powered sensor systems in the context of in-vehicle communication. The goal is to investigate the physical layer and establish an antenna recommendation system for a specific harsh environment, i.e., an engine compartment of a vehicle. We propose the usage of electromagnetic (EM) and ray tracing simulations as a computationally cost-effective method to establish such a recommendation system, which we test by means of an experimental testbed—or test environment—that consists of both a physical, as well as its identical simulation, model. A pool of antennas is evaluated to identify and verify antenna behavior and properties at specified positions in the harsh environment. We use a vector network analyzer (VNA) for accurate measurements and a received signal strength indicator (RSSI) for a first estimation of system performance. Our analysis of the experimental measurements and its EM simulation counterparts shows that both types of data lead to equivalent antenna recommendations at each of the defined positions and experimental conditions. This evaluation and verification process by measurements on an experimental testbed is important to validate the antenna recommendation process. Our results indicate that—with properly characterized antennas—such measurements can be substituted with EM simulations on an accurate EM model, which can contribute to dramatically speeding up the antenna positioning and selection process