RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

PRLS-INVES: A General Experimental Investigation Strategy for High Accuracy and Precision in Passive RFID Location Systems

Due to cost-effectiveness and easy-deployment, radio-frequency identification (RFID) location systems are widely utilized into many industrial fields, particularly in the emerging environment of the Internet of Things (IoT). High accuracy and precision are key demands for these location systems. Numerous studies have attempted to improve localization accuracy and precision using either dedicated RFID infrastructures or advanced localization algorithms. But these effects mostly consider utilization of novel RFID localization solutions rather than optimization of this utilization. Practical use of these solutions in industrial applications leads to increased cost and deployment difficulty of RFID system. This paper attempts to investigate how accuracy and precision in passive RFID location systems (PRLS) are impacted by infrastructures and localization algorithms. A general experimental-based investigation strategy, PRLS-INVES, is designed for analyzing and evaluating the factors that impact the performance of a passive RFID location system. Through a case study on passive high frequency (HF) RFID location systems with this strategy, it is discovered that: 1) the RFID infrastructure is the primary factor determining the localization capability of an RFID location system and 2) localization algorithm can improve accuracy and precision, but is limited by the primary factor. A discussion on how to efficiently improve localization accuracy and precision in passive HF RFID location systems is given

A review of RFID based solutions for indoor localization and location-based classification of tags

Wireless communication systems are very used for indoor localization of items. In particular, two main application field can be identified. The former relates to detection or localization of static items. The latter relates to real-time tracking of moving objects, whose movements can be reconstructed over identified timespans. Among the adopted technologies, Radio-Frequency IDentification (RFID), especially if based on cheap passive RFID tags, stands out for its affordability and reasonable efficiency. This aspect makes RFID suitable for both the above-mentioned applications, especially when a large number of objects need to be tagged. The reason lies in a suitable trade-off between low cost for implementing the position sensing system, and its precision and accuracy. However, RFID-based solutions suffer for limited reading range and lower accuracy. Solutions have been proposed by academia and industry. However, a structured analysis of developed solutions, useful for further implementations, is missing. The purpose of this paper is to highlight and review the recently proposed solutions for indoor localization making use of RFID passive tags. The paper focuses on both precise and qualitative location of objects. The form relates to (i) the correct position of tags, namely mapping their right position in a 2D or 3D environment. The latter relates to the classification of tags, namely the identification of the area where the tag is regardless its specific position

RFID Gazebo-Based Simulator With RSSI and Phase Signals for UHF Tags Localization and Tracking

Radio Frequency Identification (RFID) technology is becoming very popular in the new era of Industry 4.0, especially for warehouse management, retails, and logistics. RFID systems can be used for objects identification, localization, and tracking, facilitating everyday operators' efforts. However, the deployment of RFID tags and reader antennas in real-world application scenarios is crucial and takes time. Indeed, deciding where to place tags and/or readers' requires examining many conditions. If some weaknesses appear in the design, the arrangement must be reconsidered. The proposed work presents a novel open-source RFID simulator that allows modeling environments and testing the deployment of RFID tags and antennas apriori. In such a way, validating the performance of the localization or tracking algorithms in simulation, possible weaknesses that could arise may be fixed before facilities are applied on the field. Any number of tags and antennas can be placed in any position in the created scenario, and the simulator provides the phase and the RSSI signals for each tag. Every reader antenna is parametrized so that different antennas of different vendors can be reproduced. The simulator is implemented as a plugin of Gazebo, a widely used robotic framework integrated with the Robot Operating System (ROS), to reach a broad audience. In order to validate the simulator, a warehouse scenario is modeled, and a tag localization algorithm that uses the phase unwrapping technique and hyperbolae intersection method employing a reader antenna mounted on a mobile robot is used to estimate the position of the tags deployed in the scenario. The outcomes of the experiments showed realistic results

The Applicability of RFID for Indoor Localization

Chapter 11 : The applicability of RFID for indoor localizatio

Array signal processing for source localization and enhancement

“A common approach to the wide-band microphone array problem is to assume a certain array geometry and then design optimal weights (often in subbands) to meet a set of desired criteria. In addition to weights, we consider the geometry of the microphone arrangement to be part of the optimization problem. Our approach is to use particle swarm optimization (PSO) to search for the optimal geometry while using an optimal weight design to design the weights for each particle’s geometry. The resulting directivity indices (DI’s) and white noise SNR gains (WNG’s) form the basis of the PSO’s fitness function. Another important consideration in the optimal weight design are several regularization parameters. By including those parameters in the particles, we optimize their values as well in the operation of the PSO. The proposed method allows the user great flexibility in specifying desired DI’s and WNG’s over frequency by virtue of the PSO fitness function. Although the above method discusses beam and nulls steering for fixed locations, in real time scenarios, it requires us to estimate the source positions to steer the beam position adaptively. We also investigate source localization of sound and RF sources using machine learning techniques. As for the RF source localization, we consider radio frequency identification (RFID) antenna tags. Using a planar RFID antenna array with beam steering capability and using received signal strength indicator (RSSI) value captured for each beam position, the position of each RFID antenna tag is estimated. The proposed approach is also shown to perform well under various challenging scenarios”--Abstract, page iv

MULTI-SENSOR LOCALIZATION AND TRACKING IN DISASTER MANAGEMENT AND INDOOR WAYFINDING FOR VISUALLY IMPAIRED USERS

This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual and hybrid approaches to offer accurate and reliable location and orientation in complex navigation spaces. In this dissertation, significant contributions have been made in the design and implementation of various localization and tracking algorithms under different requirements of certain applications