INDOOR-WIRELESS LOCATION TECHNIQUES AND ALGORITHMS UTILIZING UHF RFID AND BLE TECHNOLOGIES

The work presented herein explores the ability of Ultra High Frequency Radio Frequency (UHF RF) devices, specifically (Radio Frequency Identification) RFID passive tags and Bluetooth Low Energy (BLE) to be used as tools to locate items of interest inside a building. Localization Systems based on these technologies are commercially available, but have failed to be widely adopted due to significant drawbacks in the accuracy and reliability of state of the art systems. It is the goal of this work to address that issue by identifying and potentially improving upon localization algorithms. The work presented here breaks the process of localization into distance estimations and trilateration algorithms to use those estimations to determine a 2D location. Distance estimations are the largest error source in trilateration. Several methods are proposed to improve speed and accuracy of measurements using additional information from frequency variations and phase angle information. Adding information from the characteristic signature of multipath signals allowed for a significant reduction in distance estimation error for both BLE and RFID which was quantified using neural network optimization techniques. The resulting error reduction algorithm was generalizable to completely new environments with very different multipath behavior and was a significant contribution of this work. Another significant contribution of this work is the experimental comparison of trilateration algorithms, which tested new and existing methods of trilateration for accuracy in a controlled environment using the same data sets. Several new or improved methods of triangulation are presented as well as traditional methods from the literature in the analysis. The Antenna Pattern Method represents a new way of compensating for the antenna radiation pattern and its potential impact on signal strength, which is also an important contribution of this effort. The performance of each algorithm for multiple types of inputs are compared and the resulting error matrix allows a potential system designer to select the best option given the particular system constraints

Human Sensing via Passive Spectrum Monitoring

Human sensing is significantly improving our lifestyle in many fields such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This paper proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed Sensing Humans among Passive Radio Frequency (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental results show that the SHAPR method achieved more than 95% accuracy in the four scenarios for the three human sensing tasks, with a localization error of less than 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability

Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms

Identifying specific locations of items such as containers, warehouse pellets, and returnable packages in a large environment, for instance, in a warehouse, requires an extensive tracking system that could identify the location through data visualization. This is the similar case for radio-frequency identification (RFID) pallet level signal as the accuracy of determining the position for specific location either on the level or stacked in the same direction are read uniformly. However, there is no single study focusing on pallet-level classification, in particular on distance measurement of pallet height. Hence, a methodological approach that could provide the solution is essential to reduce the misplaced issues and thus reduce the problem in searching the products in a large-scale setting. The objective of this work attempts to define the pallet level of the stacked RFID tags through the machine learning techniques framework. The methodology started with the pallet-level which firstly determined by manual clustering according to the product code number of the tags that were manufactured for defining the actual level. An additional study of the radio frequency of the tagged pallet box in static condition was carried out by determining the feature of the time series. Various sample sizes of 1 Hz, 5 Hz and 10 Hz combined with the received signal strength of maximum, minimum, mode, median, mean, variance, maximum and minimum difference, kurtosis and skewness are evaluated. The statistical features of the received signal strength reading are analyzed by the selection of the univariate features, feature importance technique, and principal component analysis. The received signal strength of the maximum, median, and mean of all statistical features has been shown to be significant specifically for the 10Hz sample size. Different machine learning classifiers were tested based on the significant features, namely the Artificial Neural Network, Decision Tree, Random Forest, Naive Bayes Support Vector Machine, and k-Nearest Neighbors. It was shown that up to 95.02% of the trained Random Forest Model could be classified, indicating that the established framework is viable for pallet classification. Furthermore, the efficacy of different models based on heuristic hyperparameter tuning is evaluated in which the different kernel function for Support Vector Machine, various distance metrics of k-Nearest Neighbors. The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. In results, it was found that the Random Forest provided 92.44% of the test sets with the highest accuracy. In order to further validate the position of the tagging in the pallet box of the Random Forest model developed, a different predefined location was used to validate the model. The best position that could achieve a classification accuracy of 93.30% through the validation process for position five (5) in the systematic model that is the centre of the pallet box. In conclusion, it can be inferred from the analysis that the Random Forest model has better predictive performance compared to the rest of the pallet level partition model with a height of 12 cm used in this research. Based on the train, validation, and test sets in Random Forest, the RFID capability to determine the position of the pallet can be detected precisely

Passive low frequency RFID for non-destructive evaluation and monitoring

Ph. D ThesisDespite of immense research over the years, defect monitoring in harsh environmental conditions still presents notable challenges for Non-Destructive Testing and Evaluation (NDT&E) and Structural Health Monitoring (SHM). One of the substantial challenges is the inaccessibility to the metal surface due to the large stand-off distance caused by the insulation layer. The hidden nature of corrosion and defect under thick insulation in harsh environmental conditions may result in it being not noticed and ultimately leading to failures. Generally electromagnetic NDT&E techniques which are used in pipeline industries require the removal of the insulation layer or high powered expensive equipment. Along with these, other limitations in the existing techniques create opportunities for novel systems to solve the challenges caused by Corrosion under Insulation (CUI). Extending from Pulsed Eddy Current (PEC), this research proposes the development and use of passive Low Frequency (LF) RFID hardware system for the detection and monitoring of corrosion and cracks on both ferrous and non-ferrous materials at varying high temperature conditions. The passive, low cost essence of RFID makes it an enchanting technique for long term condition monitoring. The contribution of the research work can be summarised as follows: (1) implementation of novel LF RFID sensor systems and the rig platform, experimental studies validating the detection capabilities of corrosion progression samples using transient feature analysis with respect to permeability and electrical conductivity changes along with enhanced sensitivity demonstration using ferrite sheet attached to the tag; (2) defect detection using swept frequency method to study the multiple frequency behaviour and further temperature suppression using feature fusion technique; (3) inhomogeneity study on ferrous materials at varying temperature and demonstration of the potential of the RFID system; (4) use of RFID tag with ceramic filled Poly-tetra-fluoro-ethyulene (PTFE) substrate for larger applicability of the sensing system in the industry; (5) lift-off independent defect monitoring using passive sweep frequency RFID sensors and feature extraction and fusion for robustness improvement. This research concludes that passive LF RFID system can be used to detect corrosion and crack on both ferrous and non-ferrous materials and then the system can be used to compensate for temperature variation making it useful for a wider range of applications. However, significant challenges such as permanent deployment of the tags for long term monitoring at higher temperatures and much higher standoff distance, still require improvement for real-world applicability.Engineering and Physical Sciences Research Council (EPSRC) CASE, National Nuclear Laboratory (NNL)

Signaali tugevusel põhinev esemete otsimise süsteem

In this thesis, work regarding to development a system that would help finding lost objects based on RSSI is presented. The thesis gives a basic overview of the theory behind RF modulation, antennas and PCB design. Based on the presented theory, the technology and system parameters are chosen to design and ultimately complete the system. To improve the performace of the system the RF parts of the circuits are optimized using a vector network analyzer. As a result of this thesis a periodically transmitting battery-powered beacon that works at 433.92MHz is designed and completed. To read the transmitted data and the signal strength a receiver is also designed and completed to receive the data transmitted by the beacon and display the RSSI on a smart-phone screen using the micro-USB port

Chipless RFID sensor systems for structural health monitoring

Ph. D. ThesisDefects in metallic structures such as crack and corrosion are major sources of catastrophic failures, and thus monitoring them is a crucial issue. As periodic inspection using the nondestructive testing and evaluation (NDT&E) techniques is slow, costly, limited in range, and cumbersome, novel methods for in-situ structural health monitoring (SHM) are required. Chipless radio frequency identification (RFID) is an emerging and attractive technology to implement the internet of things (IoT) based SHM. Chipless RFID sensors are not only wireless, passive, and low-cost as the chipped RFID counterpart, but also printable, durable, and allow for multi-parameter sensing. This thesis proposes the design and development of chipless RFID sensor systems for SHM, particularly for defect detection and characterization in metallic structures. Through simulation studies and experimental validations, novel metal-mountable chipless RFID sensors are demonstrated with different reader configurations and methods for feature extraction, selection, and fusion. The first contribution of this thesis is the design of a chipless RFID sensor for crack detection and characterization based on the circular microstrip patch antenna (CMPA). The sensor provides a 4-bit ID and a capability of indicating crack width and orientation simultaneously using the resonance frequency shift. The second contribution is a chipless RFID sensor designed based on the frequency selective surface (FSS) and feature fusion for corrosion characterization. The FSS-based sensor generates multiple resonance frequency features that can reveal corrosion progression, while feature fusion is applied to enhance the sensitivity and reliability of the sensor. The third contribution deals with robust detection and characterization of crack and corrosion in a realistic environment using a portable reader. A multi-resonance chipless RFID sensor is proposed along with the implementation of a portable reader using an ultra-wideband (UWB) radar module. Feature extraction and selection using principal component analysis (PCA) is employed for multi-parameter evaluation. Overall, chipless RFID sensors are small, low-profile, and can be used to quantify and characterize surface crack and corrosion undercoating. Furthermore, the multi-resonance characteristics of chipless RFID sensors are useful for integrating ID encoding and sensing functionalities, enhancing the sensor performance, as well as for performing multi-parameter analysis of defects. The demonstrated system using a portable reader shows the capability of defects characterization from a 15-cm distance. Hence, chipless RFID sensor systems have great potential to be an alternative sensing method for in-situ SHM.Indonesia Endowment Fund for Education (LPDP

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

Remote Power Analysis of {RFID} Tags

We describe the first power analysis attack on passive RFID tags. Compared to standard power analysis attacks, this attack is unique in that it requires no physical contact with the device under attack. The power analysis can be carried out even if both the tag and the attacker are passive and transmit no data, making the attack very hard to detect. As a proof of concept, we use power analysis to extract the kill passwords from Class 1 EPC tags operating in the UHF frequency range. Tags from several major vendors were successfully attacked. Our attack can be extended to HF tags and to remote fault analysis. The main significance of our attack is not in the discovery of kill passwords but in its implications on future tag design -- any cryptographic functionality built into tags needs to be designed to be resistant to power analysis, and achieving this resistance is an undertaking which has an effect both on the price and on the performance of tags. (this is my Master\u27s thesis, carried out under the supervision of Prof. Adi Shamir. It may be considered as the extended version of the article Remote Password Extraction from RFID Tags , recently published in IEEE Transactions on Computers and indexed as http://dx.doi.org/10.1109/TC.2007.1050 or as http://ieeexplore.ieee.org/iel5/12/4288079/04288095.pdf