Implementation of Middleware for Internet of Things in Asset Tracking Applications: In-lining Approach

ThesisInternet of Things (IoT) is a concept that involves giving objects a digital identity and limited artificial intelligence, which helps the objects to be interactive, process data, make decisions, communicate and react to events virtually with minimum human intervention. IoT is intensified by advancements in hardware and software engineering and promises to close the gap that exists between the physical and digital worlds. IoT is paving ways to address complex phenomena, through designing and implementation of intelligent systems that can monitor phenomena, perform real-time data interpretation, react to events, and swiftly communicate observations. The primary goal of IoT is ubiquitous computing using wireless sensors and communication protocols such as Bluetooth, Wireless Fidelity (Wi-Fi), ZigBee and General Packet Radio Service (GPRS). Insecurity, of assets and lives, is a problem around the world. One application area of IoT is tracking and monitoring; it could therefore be used to solve asset insecurity. A preliminary investigation revealed that security systems in place at Central University of Technology, Free State (CUT) are disjointed; they do not instantaneously and intelligently conscientize security personnel about security breaches using real time messages. As a result, many assets have been stolen, particularly laptops. The main objective of this research was to prove that a real-life application built over a generic IoT architecture that innovatively and intelligently integrates: (1) wireless sensors; (2) radio frequency identification (RFID) tags and readers; (3) fingerprint readers; and (4) mobile phones, can be used to dispel laptop theft. To achieve this, the researcher developed a system, using the heterogeneous devices mentioned above and a middleware that harnessed their unique capabilities to bring out the full potential of IoT in intelligently curbing laptop theft. The resulting system has the ability to: (1) monitor the presence of a laptop using RFID reader that pro-actively interrogates a passive tag attached to the laptop; (2) detect unauthorized removal of a laptop under monitoring; (3) instantly communicate security violations via cell phones; and (4) use Windows location sensors to track the position of a laptop using Googlemaps. The system also manages administrative tasks such as laptop registration, assignment and withdrawal which used to be handled manually. Experiments conducted using the resulting system prototype proved the hypothesis outlined for this research

Near Field Communication: From theory to practice

This book provides the technical essentials, state-of-the-art knowledge, business ecosystem and standards of Near Field Communication (NFC)by NFC Lab - Istanbul research centre which conducts intense research on NFC technology. In this book, the authors present the contemporary research on all aspects of NFC, addressing related security aspects as well as information on various business models. In addition, the book provides comprehensive information a designer needs to design an NFC project, an analyzer needs to analyze requirements of a new NFC based system, and a programmer needs to implement an application. Furthermore, the authors introduce the technical and administrative issues related to NFC technology, standards, and global stakeholders. It also offers comprehensive information as well as use case studies for each NFC operating mode to give the usage idea behind each operating mode thoroughly. Examples of NFC application development are provided using Java technology, and security considerations are discussed in detail. Key Features: Offers a complete understanding of the NFC technology, including standards, technical essentials, operating modes, application development with Java, security and privacy, business ecosystem analysis Provides analysis, design as well as development guidance for professionals from administrative and technical perspectives Discusses methods, techniques and modelling support including UML are demonstrated with real cases Contains case studies such as payment, ticketing, social networking and remote shopping This book will be an invaluable guide for business and ecosystem analysts, project managers, mobile commerce consultants, system and application developers, mobile developers and practitioners. It will also be of interest to researchers, software engineers, computer scientists, information technology specialists including students and graduates.Publisher's Versio

Establishing the digital chain of evidence in biometric systems

Traditionally, a chain of evidence or chain of custody refers to the chronological documentation, or paper trail, showing the seizure, custody, control, transfer, analysis, and disposition of evidence, physical or electronic. Whether in the criminal justice system, military applications, or natural disasters, ensuring the accuracy and integrity of such chains is of paramount importance. Intentional or unintentional alteration, tampering, or fabrication of digital evidence can lead to undesirable effects. We find despite the consequences at stake, historically, no unique protocol or standardized procedure exists for establishing such chains. Current practices rely on traditional paper trails and handwritten signatures as the foundation of chains of evidence.;Copying, fabricating or deleting electronic data is easier than ever and establishing equivalent digital chains of evidence has become both necessary and desirable. We propose to consider a chain of digital evidence as a multi-component validation problem. It ensures the security of access control, confidentiality, integrity, and non-repudiation of origin. Our framework, includes techniques from cryptography, keystroke analysis, digital watermarking, and hardware source identification. The work offers contributions to many of the fields used in the formation of the framework. Related to biometric watermarking, we provide a means for watermarking iris images without significantly impacting biometric performance. Specific to hardware fingerprinting, we establish the ability to verify the source of an image captured by biometric sensing devices such as fingerprint sensors and iris cameras. Related to keystroke dynamics, we establish that user stimulus familiarity is a driver of classification performance. Finally, example applications of the framework are demonstrated with data collected in crime scene investigations, people screening activities at port of entries, naval maritime interdiction operations, and mass fatality incident disaster responses

Dual-layered and wavelength-multiplexed optical barcode for high data storage

A novel barcode system design to achieve high data storage using more than one layer is introduced theoretically and tested partially in the laboratory. Compared to other existing barcode systems, diffraction gratings are used as core elements in the barcode symbol. As any other barcode system, the novel model requires a source of light, the barcode symbol and photodiode detectors. Theoretical background from optics has been used to design the entire system along with all the positioning of its components. After part-testing the design in laboratory, the barcode system design has been changed to achieve better results. Experiments have showed that the initial proposed Light Emitting Diode (LED) source light cannot deliver 5mm spot light over a range of 50cm and therefore, white Light Amplification by Stimulated Emission of Radiation (LASER) light has been adopted as replacement. The diffractions from the barcode symbol are captured by detectors built with SI photo diodes, which are designed to detect this range of wavelengths. The barcode symbol is composed of small 5mm by 5mm grating modules and the largest possible symbol size defined is 80 modules (5cmx5cm). Experimental works have proved that intensity of the light can be used to uniquely identify each grating rather than the entire spectrum diffracted. A better design is proposed where the detectors are positioned under the barcode symbol and capture the light intensity of the first diffracted order. Theoretical investigations state that diffraction gratings with different lines per mm diffract different sets of wavelengths spectrum. This characteristic allows a set of unique gratings to be used in the barcode symbol which hence allow data to be represented or stored. Character (Char) sets are defined to help encode and decode data in the barcode symbol. High data storage has been achieved through the use of two layers. Multiple layers offer the possibility to increase the number of unique sets of gratings which in turn increase the data representation capacity. Using two layers with 16 unique sets of gratings has proved to be able to store around 100 bytes of data. The system has the potential to use more than two layers and using 4 layers with 16 unique gratings per layer will achieve 200 bytes. The thesis has proved through theoretical and experimental work that diffraction gratings can be used in barcode system to represent data and multiple layers adds the benefit of increasing data storage. Further work is also suggested

Image-based Authentication

Mobile and wearable devices are popular platforms for accessing online services. However, the small form factor of such devices, makes a secure and practical experience for user authentication, challenging. Further, online fraud that includes phishing attacks, has revealed the importance of conversely providing solutions for usable authentication of remote services to online users. In this thesis, we introduce image-based solutions for mutual authentication between a user and a remote service provider. First, we propose and develop Pixie, a two-factor, object-based authentication solution for camera-equipped mobile and wearable devices. We further design ai.lock, a system that reliably extracts from images, authentication credentials similar to biometrics. Second, we introduce CEAL, a system to generate visual key fingerprint representations of arbitrary binary strings, to be used to visually authenticate online entities and their cryptographic keys. CEAL leverages deep learning to capture the target style and domain of training images, into a generator model from a large collection of sample images rather than hand curated as a collection of rules, hence provides a unique capacity for easy customizability. CEAL integrates a model of the visual discriminative ability of human perception, hence the resulting fingerprint image generator avoids mapping distinct keys to images which are not distinguishable by humans. Further, CEAL deterministically generates visually pleasing fingerprint images from an input vector where the vector components are designated to represent visual properties which are either readily perceptible to human eye, or imperceptible yet are necessary for accurately modeling the target image domain. We show that image-based authentication using Pixie is usable and fast, while ai.lock extracts authentication credentials that exceed the entropy of biometrics. Further, we show that CEAL outperforms state-of-the-art solution in terms of efficiency, usability, and resilience to powerful adversarial attacks