Dense and long-term monitoring of Earth surface processes with passive RFID -- a review

Billions of Radio-Frequency Identification (RFID) passive tags are produced yearly to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing to monitor earth surface processes. Indeed, passive tags can cost 10 to 100 times less than wireless sensors networks and require little maintenance, facilitating years-long monitoring with ten's to thousands of tags. This study reviews the existing and potential applications of RFID in geosciences. The most mature application today is the study of coarse sediment transport in rivers or coastal environments, using tags placed into pebbles. More recently, tag localization was used to monitor landslide displacement, with a centimetric accuracy. Sensing tags were used to detect a displacement threshold on unstable rocks, to monitor the soil moisture or temperature, and to monitor the snowpack temperature and snow water equivalent. RFID sensors, available today, could monitor other parameters, such as the vibration of structures, the tilt of unstable boulders, the strain of a material, or the salinity of water. Key challenges for using RFID monitoring more broadly in geosciences include the use of ground and aerial vehicles to collect data or localize tags, the increase in reading range and duration, the ability to use tags placed under ground, snow, water or vegetation, and the optimization of economical and environmental cost. As a pattern, passive RFID could fill a gap between wireless sensor networks and manual measurements, to collect data efficiently over large areas, during several years, at high spatial density and moderate cost.Comment: Invited paper for Earth Science Reviews. 50 pages without references. 31 figures. 8 table

Ultra high frequency (UHF) radio-frequency identification (RFID) for robot perception and mobile manipulation

Personal robots with autonomy, mobility, and manipulation capabilities have the potential to dramatically improve quality of life for various user populations, such as older adults and individuals with motor impairments. Unfortunately, unstructured environments present many challenges that hinder robot deployment in ordinary homes. This thesis seeks to address some of these challenges through a new robotic sensing modality that leverages a small amount of environmental augmentation in the form of Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) tags. Previous research has demonstrated the utility of infrastructure tags (affixed to walls) for robot localization; in this thesis, we specifically focus on tagging objects. Owing to their low-cost and passive (battery-free) operation, users can apply UHF RFID tags to hundreds of objects throughout their homes. The tags provide two valuable properties for robots: a unique identifier and receive signal strength indicator (RSSI, the strength of a tag's response). This thesis explores robot behaviors and radio frequency perception techniques using robot-mounted UHF RFID readers that enable a robot to efficiently discover, locate, and interact with UHF RFID tags applied to objects and people of interest. The behaviors and algorithms explicitly rely on the robot's mobility and manipulation capabilities to provide multiple opportunistic views of the complex electromagnetic landscape inside a home environment. The electromagnetic properties of RFID tags change when applied to common household objects. Objects can have varied material properties, can be placed in diverse orientations, and be relocated to completely new environments. We present a new class of optimization-based techniques for RFID sensing that are robust to the variation in tag performance caused by these complexities. We discuss a hybrid global-local search algorithm where a robot employing long-range directional antennas searches for tagged objects by maximizing expected RSSI measurements; that is, the robot attempts to position itself (1) near a desired tagged object and (2) oriented towards it. The robot first performs a sparse, global RFID search to locate a pose in the neighborhood of the tagged object, followed by a series of local search behaviors (bearing estimation and RFID servoing) to refine the robot's state within the local basin of attraction. We report on RFID search experiments performed in Georgia Tech's Aware Home (a real home). Our optimization-based approach yields superior performance compared to state of the art tag localization algorithms, does not require RF sensor models, is easy to implement, and generalizes to other short-range RFID sensor systems embedded in a robot's end effector. We demonstrate proof of concept applications, such as medication delivery and multi-sensor fusion, using these techniques. Through our experimental results, we show that UHF RFID is a complementary sensing modality that can assist robots in unstructured human environments.PhDCommittee Chair: Kemp, Charles C.; Committee Member: Abowd, Gregory; Committee Member: Howard, Ayanna; Committee Member: Ingram, Mary Ann; Committee Member: Reynolds, Matt; Committee Member: Tentzeris, Emmanoui

Developing a person guidance module for hospital robots

This dissertation describes the design and implementation of the Person Guidance Module (PGM) that enables the IWARD (Intelligent Robot Swarm for attendance, Recognition, Cleaning and delivery) base robot to offer route guidance service to the patients or visitors inside the hospital arena. One of the common problems encountered in huge hospital buildings today is foreigners not being able to find their way around in the hospital. Although there are a variety of guide robots currently existing on the market and offering a wide range of guidance and related activities, they do not fit into the modular concept of the IWARD project. The PGM features a robust and foolproof non-hierarchical sensor fusion approach of an active RFID, stereovision and cricket mote sensor for guiding a patient to the X-ray room, or a visitor to a patient’s ward in every possible scenario in a complex, dynamic and crowded hospital environment. Moreover, the speed of the robot can be adjusted automatically according to the pace of the follower for physical comfort using this system. Furthermore, the module performs these tasks in any unconstructed environment solely from a robot’s onboard perceptual resources in order to limit the hardware installation costs and therefore the indoor setting support. Similar comprehensive solution in one single platform has remained elusive in existing literature. The finished module can be connected to any IWARD base robot using quick-change mechanical connections and standard electrical connections. The PGM module box is equipped with a Gumstix embedded computer for all module computing which is powered up automatically once the module box is inserted into the robot. In line with the general software architecture of the IWARD project, all software modules are developed as Orca2 components and cross-complied for Gumstix’s XScale processor. To support standardized communication between different software components, Internet Communications Engine (Ice) has been used as middleware. Additionally, plug-and-play capabilities have been developed and incorporated so that swarm system is aware at all times of which robot is equipped with PGM. Finally, in several field trials in hospital environments, the person guidance module has shown its suitability for a challenging real-world application as well as the necessary user acceptance

An 8-bit UHF RFID tag for passive sensing applications

This paper presents a passive UHF RFID tag consisting of a single dipole antenna matched to multiple RFID devices, where each is programmed with a unique binary weighting in its EPC register. With each device having its own digital input this results in a single tag capable of binary counting or a tag with multiple inputs. Measurement results and associated reader software demonstrate a proof of concept for an 8-bit version of the tag

Carbon Nanotube Loaded Passive UHF RFID Sensor Tag with Built-in Reference for Wireless Gas Sensing

Radio Frequency IDentification (RFID) technology, which uses communication by means of reflected power, is used for the wireless identification of objects. Individual objects are identified by the RFID tag placed on them. An RFID tag consists of a microchip and an antenna. An RFID reader transmits radio frequency (RF) waves to identify the tagged objects. It transmits identification information, which is stored in its memory, through back scattered radio waves to the RFID reader. Passive RFID tags harvests RF energy from the reader device to power its microchip, enabling battery free operation. Passive wireless sensors based on UHF RFID technology are a promising prospect in the realm of ubiquitous sensing and Internet of Things (IoT). The sensing principles and methods used depend on the variation of the tag antenna gain, the impedance match between the tag antenna and the RFID chip, or both, with respect to the sensed parameter. The RFID reader uses back scattered RF signal properties to perform sensing. Usually, threshold power, the power at which an RFID tag harvests enough power to turn itself ON, or back scattered signal power, is used for sensing measurements. These measurements depend heavily on the environment, where the tag is placed, and the distance at which it is measured by a reader. This poses severe restrictions in sensing measurements. To maintain sensor accuracy, precise calibration of the measurement setup is required. Any disturbance in the measurement setup or the RF propagation environment affect the sensor measurement. This thesis presents a novel architecture of inkjet-printed passive UHF RFID based sensor tag that allows a reference measurement and sensing measurement for wireless gas sensing. In this work, an RFID tag is made with Silver (Ag) ink, and is loaded with carbon nanotube (CNT) ink for sensing purpose. Carbon nanotubes (CNT) have a property that it modifies its conductivity in the presence of certain gases. This property is exploited for sensing (CO2) gas. A switch, used in the sensor tag’s structure, provides two modes of operation. They are, sensor on (SON) or sensing mode operation, and sensor off (SOFF) or reference mode operation. In SON mode, the sensor tag modifies its backscatter properties in the presence of gas. In SOFF mode, the realized gain of the sensor tag remains constant in the presence of gas, which provides a reference measurement. The difference in threshold power, between SON mode and SOFF mode is used as the sensing parameter. This sensing paradigm allows sensor measurements that do not depend on the RF propagation conditions, or the distance of the reader. The fabricated sensor tags, when exposed to CO2, show a threshold power variation of up to 2dB, with a read range of about 4m at 915MHz. This means, threshold power difference between SON and SOFF mode provides unambiguous detection of CO2 at all measurement conditions. Study and measurements done in this work prove the feasibility of gas detection by placing CNT very close to the tag, instead of, on the tag. More importantly, the concept of using a switch in the sensor tag to provide reference measurement is proven. Several possibilities exist in the realization of the switch including, but not limited to, incorporating the switch within the RFID chip. These ideas will be explored in future work

Capacitive coupled RFID tag using a new dielectric droplet encapsulation approach

Radio frequency identification (RFID) is a well-known and fast-growing technology used to identify people, animals and products. RFID tags are used to replace bar codes in a wide range of applications, to mention just a few, retail, transportation, logistics and healthcare. The two main driving aspects for most of research and development projects concerning RFID tags are the reduction of assembly costs and the downsizing of microchips. In that respect and considering an Industry 4.0 scenario, the study of a new assembly approach for passive and high frequency RFID tags has been proposed and studied in this thesis. In this new approach, which is based on the inkjet printing technology, a specifically designed radio frequency integrated circuit (RFIC) will be delivered, inside a liquid dielectric droplet, onto the antenna and no longer placed and oriented precisely as it happens nowadays with pick-and-place and flip chip machines. After a landing phase, the liquid droplet (with the encapsulated chip) will self-aligns with respect to the contact thanks to capillary forces driven by specifically designed wetting conditions on the substrate of the antenna. Finally, with few additional steps, the complete RFID tag is created. This research project brings to light a considerable simplification and a very high potential of parallelization, compatible with large volume manufacturing methods, in comparison to nowadays existing technologies. This may substantially drive down the fabrication costs. An in-depth analysis of electrical performances have been carefully undertaken and compliance with the ISO/IEC 144443 standard has been verified. Mathematical models have been developed showing fundamental limits for the maximum tag reading range and power requirements of the RFID reader

DISTRIBUTED PHASED ARRAY ANTENNAS IN WIDE AREA RFID

Ultra High Frequency (UHF) Radio Frequency Identification (RFID) has gained importance over the past two decades in many applications such as stock management, asset tracking and access control. For wide area applications, Distributed Antenna Systems (DAS) have been used to obtain good coverage with few antennas by making use of multiple spatially distributed antennas and phase dithering. This implements a far-field beamforming that maximises the instantaneous power at a tag. Separately, phased array antennas have also been used to increase the read range by increasing the effective field of view of an antenna and overcoming multipath fading through beam steering. This dissertation explores a combination of both approaches to improve RFID read ranges in wide interrogation zones. Distributed antenna arrays are explored in the context of delivering high tag detection probabilities in a multi-cell RFID system, while maximising inter-antenna separations. A Distributed Antenna Array System (DAAS) is designed and shown to be capable of providing comparable performance to a fixed DAS system with fewer antennas. The properties of the system are further studied and its upper performance limit is explored by modelling a hypothetical perfectly steerable antenna array. The concept of using perfectly steerable arrays is further explored to propose a cell-less RFID system, in which cell allocation in wide area RFID is replaced with a tag location-based interrogation requiring the global reader antenna population to be used for interrogation of all tags, leading to significant potential increases in inter-antenna separation, and consequently good coverage with fewer antennas. It is also argued that this system leads to the avoidance of complex reader anti-collision policies, since only a single central reader is now required. Finally, the design of a wide-scan-angle antenna array is presented as a compromise solution for perfectly steerable antennas, whist still keeping the desired property of being flat panel. A 3D RFID multi-antenna model is presented and used for simulating and analysing the various described systems and for system planning

Design And Implementation Of Motion Sensitive UHF RFID System

Radio Frequency Identification (RFID) is a method of remotely storing and retrieving data using devices called RFID tags. RFID system components comprise of RFID tag and RFID reader. The RFID tag stores a unique identification of the object that is attached to it. However, it does not provide information about the conditions of the object that it detects. Sensor node in Wireless Sensor Network (WSN), on the other hand, provides information about the condition of the object and its environment. Therefore, with the integration of RFID and WSN technology, their disadvantages can be overcome and their advantages can be put into some important applications

Applications of Antenna Technology in Sensors

During the past few decades, information technologies have been evolving at a tremendous rate, causing profound changes to our world and to our ways of living. Emerging applications have opened u[ new routes and set new trends for antenna sensors. With the advent of the Internet of Things (IoT), the adaptation of antenna technologies for sensor and sensing applications has become more important. Now, the antennas must be reconfigurable, flexible, low profile, and low-cost, for applications from airborne and vehicles, to machine-to-machine, IoT, 5G, etc. This reprint aims to introduce and treat a series of advanced and emerging topics in the field of antenna sensors

Ambulatory Monitoring Using Passive RFID Technology

Human activity recognition using wearable sensors is a growing field of study in pervasive computing that forms the basis for ubiquitous applications in areas like health care, manufacturing, human computer interaction and sports. A new generation of passive (batteryless) sensors such as sensor enabled RFID (Radio Frequency Identification) tags are creating new prospects for wearable sensor based applications. As passive sensors are lightweight and small, they can be used for unobtrusive monitoring. Furthermore, these sensors are maintenance free as they require no battery. However, recognising activities from passive sensor enabled RFID tags is challenging due to the sparse and noisy nature of the data streams from these sensors because they need to harvest adequate energy for successful operation. Therefore, within this thesis, we propose methods to recognise activities in real time using passive RFID technology by alleviating the adverse effects of sparsity and noise. We mainly consider ambulatory monitoring to facilitate mitigating falls in hospitals and older care settings as our application context. Specifically, three aspects are considered: i) data acquisition from sensor enabled RFID tags; ii) monitoring ambulatory movements using passive sensor enabled RFID tags to recognise activities leading to falls; and iii) detecting falls using a dense deployment of passive RFID tags. A generic middleware architecture and a generic tag ID format to embed sensor data and uniquely identify tag capabilities are proposed to acquire sensor data from passive sensor enabled RFID tags. The characteristics of this middleware are established using experiments with RFID readers and an example application scenario. In the context of ambulatory monitoring using passive sensor enabled RFID tags, first, an algorithm to facilitate the online interpolation of sparse accelerometer data from passive sensor enabled RFID tags is proposed followed by an investigation of features for activity recognition. Secondly, two data stream segmentation methods are proposed that can segment the data stream on possible activity boundaries to mitigate the adverse effects posed by data stream sparsity on segmentation. Thirdly, an algorithm to model the sequential nature considering previous sensor observations for a given time and their class labels to classify a sparse data stream in real time is proposed. Finally, a classification algorithm based on structured prediction is proposed to both segment and classify the sensor data stream simultaneously. The proposed methods are evaluated using four datasets that have been collected from a passive sensor enabled RFID tag with an accelerometer and successful monitoring of ambulatory movements is demonstrated to be possible by employing innovative data stream processing methods, based on machine learning. In order to detect falls, particularly long lie situation, using a dense deployment of passive RFID tags embedded in a carpet, an efficient and scalable machine learning based algorithm is proposed. This algorithm relies only on binary tag observation information. First, it identifies possible fall locations using heuristics and then the falls are identified using machine learning from features extracted considering possible fall locations alone. From an evaluation, it is demonstrated that the proposed algorithm could successfully identify falls in real time.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201