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

RFID signal acquisition and identification

RFID is one of the fastest growing technologies grouped under Automatic Identification (auto ID). RFID tags are very low cost and used for identification of objects. RFID is a global technology that is used in industries, medical, wall mart, airport baggage, Libraries, Smart cards, even in every transported object has its own RFID tag. Therefore, concern of security and privacy should be there to prevent unauthorized access. A method is proposed to prevent cloning and counterfeiting of tags based on RF Fingerprinting. RF fingerprinting of a tag is based upon physical attributed such as an electromagnetic (EM) signal of the tag. By capturing the EM signal of RFID tags a method known as Dynamic wavelet fingerprinting is applied to generate fingerprint images of signals. Our proposed method consists of four stages: Namely Real time data acquiring by use of a CRO, Dynamic wavelet fingerprinting (DWFP) of the signal, Feature extraction, and Classification. Feature is extracted such as Eccentricity, perimeter, centroid , extent, area and orientation. Ann classifier is used which is a one vs. One classifier. To improve the performance of classification multi-feature based serial feature fusion technique has been proposed, which shows a significant improvement in classification performance. RF fingerprint allows prevention of unauthorized access, identification and detecting cloning of sensitive devices. To identify tags and to detect counterfeit RF fingerprinting can be used. The cost of the tag does not increase and can be used in existing tag with only requirement of softwar

Wireless Device Authentication Techniques Using Physical-Layer Device Fingerprint

Due to the open nature of the radio signal propagation medium, wireless communication is inherently more vulnerable to various attacks than wired communication. Consequently, communication security is always one of the critical concerns in wireless networks. Given that the sophisticated adversaries may cover up their malicious behaviors through impersonation of legitimate devices, reliable wireless authentication is becoming indispensable to prevent such impersonation-based attacks through verification of the claimed identities of wireless devices. Conventional wireless authentication is achieved above the physical layer using upper-layer identities and key-based cryptography. As a result, user authenticity can even be validated for the malicious attackers using compromised security key. Recently, many studies have proven that wireless devices can be authenticated by exploiting unique physical-layer characteristics. Compared to the key-based approach, the possession of such physical-layer characteristics is directly associated with the transceiver\u27s unique radio-frequency hardware and corresponding communication environment, which are extremely difficult to forge in practice. However, the reliability of physical-layer authentication is not always high enough. Due to the popularity of cooperative communications, effective implementation of physical-layer authentication in wireless relay systems is urgently needed. On the other hand, the integration with existing upper-layer authentication protocols still has many challenges, e.g., end-to-end authentication. This dissertation is motivated to develop novel physical-layer authentication techniques in addressing the aforementioned challenges. In achieving enhanced wireless authentication, we first specifically identify the technique challenges in authenticating cooperative amplify-and-forward (AF) relay. Since AF relay only works at the physical layer, all of the existing upper-layer authentication protocols are ineffective in identifying AF relay nodes. To solve this problem, a novel device fingerprint of AF relay consisting of wireless channel gains and in-phase and quadrature imbalances (IQI) is proposed. Using this device fingerprint, satisfactory authentication accuracy is achieved when the signal-to-noise ratio is high enough. Besides, the optimal AF relay identification system is studied to maximize the performance of identifying multiple AF relays in the low signal-to-noise regime and small IQI. The optimal signals for quadrature amplitude modulation and phase shift keying modulations are derived to defend against the repeated access attempts made by some attackers with specific IQIs. Exploring effective authentication enhancement technique is another key objective of this dissertation. Due to the fast variation of channel-based fingerprints as well as the limited range of device-specific fingerprints, the performance of physical-layer authentication is not always reliable. In light of this, the physical-layer authentication is enhanced in two aspects. On the one hand, the device fingerprinting can be strengthened by considering multiple characteristics. The proper characteristics selection strategy, measurement method and optimal weighted combination of the selected characteristics are investigated. On the other hand, the accuracy of fingerprint estimation and differentiation can be improved by exploiting diversity techniques. To be specific, cooperative diversity in the form of involving multiple collaborative receivers is used in differentiating both frequency-dependent and frequency-independent device fingerprints. As a typical combining method of the space diversity techniques, the maximal-ratio combining is also applied in the receiver side to combat the channel degeneration effect and increase the fingerprint-to-noise ratio. Given the inherent weaknesses of the widely utilized upper-layer authentication protocols, it is straightforward to consider physical-layer authentication as an effective complement to reinforce existing authentication schemes. To this end, a cross-layer authentication is designed to seamlessly integrate the physical-layer authentication with existing infrastructures and protocols. The specific problems such as physical-layer key generation as well as the end-to-end authentication in networks are investigated. In addition, the authentication complexity reduction is also studied. Through prediction, pre-sharing and reusing the physical-layer information, the authentication processing time can be significantly shortened

Authorized and rogue device discrimination using dimensionally reduced RF-DNA fingerprints for security purposes in wireless communication systems

La nature des réseaux de capteurs sans fil comme ZigBee, permettant la communication entre différents types de nœuds du réseau, les rend très vulnérables à divers types de menaces. Dans différentes applications des technologies sans fil modernes comme SmartHome, les informations privées et sensibles produites par le réseau peuvent être transmises au monde extérieur par des moyens filaires ou sans fil. Outre les avantages offerts, cette intégration augmentera certainement les exigences en matière de protection des communications. Les nœuds capteurs du réseau étant souvent placés à proximité d'autres appareils, le réseau peut être plus vulnérable aux attaques potentielles. Cette recherche de doctorat a pour but d'utiliser les attributs natifs distincts de radiofréquence RF-DNA sécurisés produits par le processus d'empreinte numérique dans le but de fournir un support de communication sans fil sécurisé pour les communications de réseau ZigBee. Ici, nous visons à permettre une discrimination d'appareil en utilisant des préambules physiques (PHY) extraits des signaux émis pas de différents appareils. Grâce à cette procédure, nous pouvons établir une distinction entre différents appareils produits par différents fabricants ou par le même fabricant. Dans un tel cas, nous serons en mesure de fournir aux appareils des identifications physiques de niveau binaire non clonables qui empêchent l'accès non autorisé des appareils non autorisés au réseau par la falsification des identifications autorisées.The nature of wireless networks like ZigBee sensors, being able to provide communication between different types of nodes in the network makes them very vulnerable to various types of threats. In different applications of modern wireless technologies like Smart Home, private and sensitive information produced by the network can be conveyed to the outside world through wired or wireless means. Besides the advantages, this integration will definitely increase the requirements in the security of communications. The sensor nodes of the network are often located in the accessible range of other devices, and in such cases, a network may face more vulnerability to potential attacks. This Ph.D. research aims to use the secure Radio Frequency Distinct Native Attributes (RF-DNA) produced by the fingerprinting process to provide a secure wireless communication media for ZigBee network device communications. Here, we aim to provide device discrimination using Physical (PHY) preambles extracted from the signal transmitted by different devices. Through this procedure, we are able to distinguish between different devices produced by different manufacturers, or by the same one. In such cases, we will be able to provide devices with unclonable physical bit-level identifications that prevent the unauthorized access of rogue devices to the network through the forgery of authorized devices' identifications