Security and Privacy in RFID Applications

Concerns about privacy and security may limit the deployment of RFID technology and its benefits, therefore it is important they are identified and adequately addressed. System developers and other market actors are aware of the threats and are developing a number of counter measures. RFID systems can never be absolutely secure but effort needs to be made to ensure a proper balance between the risks and the costs of counter measures. The approach taken to privacy and security should depend on the application area and the context of a specific application. In this chapter, we selected and discussed four application areas, but there are many others where privacy and security issues are relevant.JRC.J.4-Information Societ

Passive RFID Rotation Dimension Reduction via Aggregation

Radio Frequency IDentification (RFID) has applications in object identification, position, and orientation tracking. RFID technology can be applied in hospitals for patient and equipment tracking, stores and warehouses for product tracking, robots for self-localisation, tracking hazardous materials, or locating any other desired object. Efficient and accurate algorithms that perform localisation are required to extract meaningful data beyond simple identification. A Received Signal Strength Indicator (RSSI) is the strength of a received radio frequency signal used to localise passive and active RFID tags. Many factors affect RSSI such as reflections, tag rotation in 3D space, and obstacles blocking line-of-sight. LANDMARC is a statistical method for estimating tag location based on a target tag’s similarity to surrounding reference tags. LANDMARC does not take into account the rotation of the target tag. By either aggregating multiple reference tag positions at various rotations, or by determining a rotation value for a newly read tag, we can perform an expected value calculation based on a comparison to the k-most similar training samples via an algorithm called K-Nearest Neighbours (KNN) more accurately. By choosing the average as the aggregation function, we improve the relative accuracy of single-rotation LANDMARC localisation by 10%, and any-rotation localisation by 20%

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

Localisation en intérieur et gestion de la mobilité dans les réseaux sans fils hétérogènes émergents

Au cours des dernières décennies, nous avons été témoins d'une évolution considérable dans l'informatique mobile, réseau sans fil et des appareils portatifs. Dans les réseaux de communication à venir, les utilisateurs devraient être encore plus mobiles exigeant une connectivité omniprésente à différentes applications qui seront de préférence au courant de leur contexte. Certes, les informations de localisation dans le cadre de leur contexte est d'une importance primordiale à la fois la demande et les perspectives du réseau. Depuis l'application ou de point de vue utilisateur, la fourniture de services peut mettre à jour si l'adaptation au contexte de l'utilisateur est activée. Du point de vue du réseau, des fonctionnalités telles que le routage, la gestion de transfert, l'allocation des ressources et d'autres peuvent également bénéficier si l'emplacement de l'utilisateur peuvent être suivis ou même prédit. Dans ce contexte, nous nous concentrons notre attention sur la localisation à l'intérieur et de la prévision transfert qui sont des composants indispensables à la réussite ultime de l'ère de la communication omniprésente envisagé. Alors que les systèmes de positionnement en plein air ont déjà prouvé leur potentiel dans un large éventail d'applications commerciales, le chemin vers un système de localisation à l'intérieur de succès est reconnu pour être beaucoup plus difficile, principalement en raison des caractéristiques difficiles à l'intérieur et l'exigence d'une plus grande précision. De même, la gestion de transfert dans le futur des réseaux hétérogènes sans fil est beaucoup plus difficile que dans les réseaux traditionnels homogènes. Régimes de procédure de transfert doit être sans faille pour la réunion strictes de qualité de service (QoS) des applications futures et fonctionnel malgré la diversité des caractéristiques de fonctionnement des différentes technologies. En outre, les décisions transfert devraient être suffisamment souples pour tenir compte des préférences utilisateur d'un large éventail de critères proposés par toutes les technologies. L'objectif principal de cette thèse est de mettre au point précis, l'heure et l'emplacement de puissance et de systèmes efficaces de gestion de transfert afin de mieux satisfaire applications sensibles au contexte et mobiles. Pour obtenir une localisation à l'intérieur, le potentiel de réseau local sans fil (WLAN) et Radio Frequency Identification (RFID) que l'emplacement autonome technologies de détection sont d'abord étudiés par des essais plusieurs algorithmes et paramètres dans un banc d'essai expérimental réel ou par de nombreuses simulations, alors que leurs lacunes sont également été identifiés. Leur intégration dans une architecture commune est alors proposée afin de combiner leurs principaux avantages et surmonter leurs limitations. La supériorité des performances du système de synergie sur le stand alone homologues est validée par une analyse approfondie. En ce qui concerne la tâche de gestion transfert, nous repérer que la sensibilité au contexte peut aussi améliorer la fonctionnalité du réseau. En conséquence, deux de tels systèmes qui utilisent l'information obtenue à partir des systèmes de localisation sont proposées. Le premier schéma repose sur un déploiement tag RFID, comme notre architecture de positionnement RFID, et en suivant la scène WLAN analyse du concept de positionnement, prédit l'emplacement réseau de la prochaine couche, c'est à dire le prochain point de fixation sur le réseau. Le second régime repose sur une approche intégrée RFID et sans fil de capteur / actionneur Network (WSAN) de déploiement pour la localisation des utilisateurs physiques et par la suite pour prédire la prochaine leur point de transfert à deux couches de liaison et le réseau. Etre indépendant de la technologie d'accès sans fil principe sous-jacent, les deux régimes peuvent être facilement mises en œuvre dans des réseaux hétérogènes [...]Over the last few decades, we have been witnessing a tremendous evolution in mobile computing, wireless networking and hand-held devices. In the future communication networks, users are anticipated to become even more mobile demanding for ubiquitous connectivity to different applications which will be preferably aware of their context. Admittedly, location information as part of their context is of paramount importance from both application and network perspectives. From application or user point of view, service provision can upgrade if adaptation to the user's context is enabled. From network point of view, functionalities such as routing, handoff management, resource allocation and others can also benefit if user's location can be tracked or even predicted. Within this context, we focus our attention on indoor localization and handoff prediction which are indispensable components towards the ultimate success of the envisioned pervasive communication era. While outdoor positioning systems have already proven their potential in a wide range of commercial applications, the path towards a successful indoor location system is recognized to be much more difficult, mainly due to the harsh indoor characteristics and requirement for higher accuracy. Similarly, handoff management in the future heterogeneous wireless networks is much more challenging than in traditional homogeneous networks. Handoff schemes must be seamless for meeting strict Quality of Service (QoS) requirements of the future applications and functional despite the diversity of operation features of the different technologies. In addition, handoff decisions should be flexible enough to accommodate user preferences from a wide range of criteria offered by all technologies. The main objective of this thesis is to devise accurate, time and power efficient location and handoff management systems in order to satisfy better context-aware and mobile applications. For indoor localization, the potential of Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) technologies as standalone location sensing technologies are first studied by testing several algorithms and metrics in a real experimental testbed or by extensive simulations, while their shortcomings are also identified. Their integration in a common architecture is then proposed in order to combine their key benefits and overcome their limitations. The performance superiority of the synergetic system over the stand alone counterparts is validated via extensive analysis. Regarding the handoff management task, we pinpoint that context awareness can also enhance the network functionality. Consequently, two such schemes which utilize information obtained from localization systems are proposed. The first scheme relies on a RFID tag deployment, alike our RFID positioning architecture, and by following the WLAN scene analysis positioning concept, predicts the next network layer location, i.e. the next point of attachment to the network. The second scheme relies on an integrated RFID and Wireless Sensor/Actuator Network (WSAN) deployment for tracking the users' physical location and subsequently for predicting next their handoff point at both link and network layers. Being independent of the underlying principle wireless access technology, both schemes can be easily implemented in heterogeneous networks. Performance evaluation results demonstrate the advantages of the proposed schemes over the standard protocols regarding prediction accuracy, time latency and energy savingsEVRY-INT (912282302) / SudocSudocFranceF

Design of Systems and Optimizations for Autonomous Agents using passive RFID Localization Techniques - Recycling Collaborative Robots

This paper aims to describe the work done towards designing and implementing systems and optimizations for a set of autonomous robots that intend to collaborate towards accomplishing the specific common goal of transporting recycled objects. At first the paper dives into the aspects of autonomous behavior and describes what exactly constitutes autonomous behavior and then proceeds to explain the specifics of the research work in our lab at Georgia Tech and also mentions the importance and reasons behind performing such research. The paper then goes into an extensive literature review of autonomous collaborative topics and puts emphasis on RFID localization techniques. And finally describes the results and discusses the outcomes of the project. Having the research abruptly paused due to the COVID-19 pandemic in Spring of 2020, prevented us from getting to implement the collaborative medium for the robots and putting into a software service box for shipment, however we were able to discover many new findings in the fields of autonomous behavior development and implement a successful and consistent RFID reader-tag duo for our robots to be next used in implementing a collaborative medium for the robots. Special thanks and gratitude towards professors, advisors, UROP representatives and instructors, and graduate students who helped me and our research group in conducting this great research.Undergraduat

Puolivalvottu WLAN-radiokarttojen oppiminen

In this thesis a manifold learning method is applied to the problem of WLAN positioning and automatic radio map creation. Due to the nature of WLAN signal strength measurements, a signal map created from raw measurements results in non-linear distance relations between measurement points. These signal strength vectors reside in a high-dimensioned coordinate system. With the help of the so called Isomap-algorithm the dimensionality of this map can be reduced, and thus more easily processed. By embedding position-labeled strategic key points, we can automatically adjust the mapping to match the surveyed environment. The environment is thus learned in a semi-supervised way; gathering training points and embedding them in a two-dimensional manifold gives us a rough mapping of the measured environment. After a calibration phase, where the labeled key points in the training data are used to associate coordinates in the manifold representation with geographical locations, we can perform positioning using the adjusted map. This can be achieved through a traditional supervised learning process, which in our case is a simple nearest neighbors matching of a sampled signal strength vector. We deployed this system in two locations in the Kumpula campus in Helsinki, Finland. Results indicate that positioning based on the learned radio map can achieve good accuracy, especially in hallways or other areas in the environment where the WLAN signal is constrained by obstacles such as walls.Työssä sovelletaan monisto-oppimismenetelmää WLAN-paikannuksen ja automaattisen radiokartan luonnin ongelmaan. WLAN-signaalivoimakkuuksien mittausten luonteen takia käsittelemättömät mittaukset aiheuttavat epälineaarisia suhteita radiokartan mittauspisteiden välille. Nämä signaalivoimakkuusvektorit sijaitsevat avaruudessa jolla on korkea ulottuvuus. Niin kutsutun Isomap-algoritmin avulla kartan ulottuvuuksia voidaan karsia, jolloin sitä on helpompi työstää. Upottamalla karttaan merkittyjä avainpisteitä, se voidaan automaattisesti säätää vastaamaan mitattua ympäristöä. Ympäristö siis opitaan puolivalvotusti; keräämällä harjoituspisteitä ja upottamalla ne kaksiulotteiseen monistoon saadaan karkea kartta ympäristöstä. Kalibrointivaiheen jälkeen, jossa merkittyjä avainpisteitä käytetään yhdistämään moniston koordinaatit maantieteellisiin kohteisiin, voidaan suorittaa paikannusta säädetyn kartan avulla. Tämä voidaan tehdä perinteisen valvotun oppimisen avulla, joka tässä tapauksessa on yksinkertainen lähimmän naapurin löytäminen mitatulle signaalivoimakkuusvektorille. Järjestelmää kokeiltiin kahdessa paikassa Kumpulan kampuksessa Helsingissä. Tulokset viittaavat siihen että opitun radiokartan avulla paikannus voi saavuttaa hyvän tarkkuuden, etenkin käytävissä ja muissa tiloissa jossa esteet kuten seinät rajoittavat WLAN-signaalia