Generic and Parameterizable Service for Remote Configuration of Mobile Phones Using Near Field Communication

Os serviços nos nossos dispositivos móveis têm aumentado em número e complexidade nos últimos anos. Utilizadores menos experientes sentem dificuldade em tirar total partido destes serviços. De forma a atenuar este problema, é necessário encontrar novas e inovadoras formas que permitam assistir o utilizador no processo de configuração. Para além disso, vivemos numa sociedade do imediato. As pessoas querem que o acesso aos recursos seja rápido, simples e seguro. É também sabido que grande parte dos utilizadores são leigos no que diz respeito à utilização de funcionalidades avançadas dos dispositivos móveis, o que resulta em alguma inércia no uso de certas aplicações e funcionalidades.O Near Field Communication oferece uma oportunidade única para introduzir novos paradigmas de negócio no que diz respeito à interação e facilidade de utilização. Esta dissertação especifica um serviço genérico e parametrizável para a configuração remota de dispositivos.Mobile services have increased both in number and complexity in the past few years. This means that in order to get the most out of these services, less experienced users will have a hard time configuring them by hand. To address this issue, we must find new and innovative solutions to assist the user in this process. Furthermore, we live in a society of the immediate. Everyone wants access to resources to be fast, simple and secure. It is also known that most of the users are laymen when referring to advanced configuration of mobile phone, resulting in some inertia in the use of applications and functionalities.Near Field Communication (NFC) provides an unique opportunity to introduce new business paradigms in terms of interaction and ease of use. This dissertation specifies a generic and parameterizable service for remote configuration of mobile devices using Near Field Communication, which requires minimal user intervention

INDIGO: a generalized model and framework for performance prediction of data dissemination

According to recent studies, an enormous rise in location-based mobile services is expected in future. People are interested in getting and acting on the localized information retrieved from their vicinity like local events, shopping offers, local food, etc. These studies also suggested that local businesses intend to maximize the reach of their localized offers/advertisements by pushing them to the maxi- mum number of interested people. The scope of such localized services can be augmented by leveraging the capabilities of smartphones through the dissemination of such information to other interested people. To enable local businesses (or publishers) of localized services to take in- formed decision and assess the performance of their dissemination-based localized services in advance, we need to predict the performance of data dissemination in complex real-world scenarios. Some of the questions relevant to publishers could be the maximum time required to disseminate information, best relays to maximize information dissemination etc. This thesis addresses these questions and provides a solution called INDIGO that enables the prediction of data dissemination performance based on the availability of physical and social proximity information among people by collectively considering different real-world aspects of data dissemination process. INDIGO empowers publishers to assess the performance of their localized dissemination based services in advance both in physical as well as the online social world. It provides a solution called INDIGO–Physical for the cases where physical proximity plays the fundamental role and enables the tighter prediction of data dissemination time and prediction of best relays under real-world mobility, communication and data dissemination strategy aspects. Further, this thesis also contributes in providing the performance prediction of data dissemination in large-scale online social networks where the social proximity is prominent using INDIGO–OSN part of the INDIGO framework under different real-world dissemination aspects like heterogeneous activity of users, type of information that needs to be disseminated, friendship ties and the content of the published online activities. INDIGO is the first work that provides a set of solutions and enables publishers to predict the performance of their localized dissemination based services based on the availability of physical and social proximity information among people and different real-world aspects of data dissemination process in both physical and online social networks. INDIGO outperforms the existing works for physical proximity by providing 5 times tighter upper bound of data dissemination time under real-world data dissemination aspects. Further, for social proximity, INDIGO is able to predict the data dissemination with 90% accuracy and differently, from other works, it also provides the trade-off between high prediction accuracy and privacy by introducing the feature planes from an online social networks

Re-Identification Attacks – A Systematic Literature Review

The publication of increasing amounts of anonymised open source data has resulted in a worryingly rising number of successful re-identification attacks. This has a number of privacy and security implications both on an individual and corporate level. This paper uses a Systematic Literature Review to investigate the depth and extent of this problem as reported in peer reviewed literature. Using a detailed protocol ,seven research portals were explored, 10,873 database entries were searched, from which a subset of 220 papers were selected for further review. From this total, 55 papers were selected as being within scope and to be included in the final review. The main review findings are that 72.7% of all successful re-identification attacks have taken place since 2009. Most attacks use multiple datasets. The majority of them have taken place on global datasets such as social networking data, and have been conducted by US based researchers. Furthermore, the number of datasets can be used as an attribute. Because privacy breaches have security, policy and legal implications (e.g. data protection, Safe Harbor etc.), the work highlights the need for new and improved anonymisation techniques or indeed, a fresh approach to open source publishing

SLS: Smart localization service: human mobility models and machine learning enhancements for mobile phone’s localization

In recent years we are witnessing a noticeable increment in the usage of new generation smartphones, as well as the growth of mobile application development. Today, there is an app for almost everything we need. We are surrounded by a huge number of proactive applications, which automatically provide relevant information and services when and where we need them. This switch from the previous generation of passive applications to the new one of proactive applications has been enabled by the exploitation of context information. One of the most important and most widely used pieces of context information is location data. For this reason, new generation devices include a localization engine that exploits various embedded technologies (e.g., GPS, WiFi, GSM) to retrieve location information. Consequently, the key issue in localization is now the efficient use of the mobile localization engine, where efficient means lightweight on device resource consumption, responsive, accurate and safe in terms of privacy. In fact, since the device resources are limited, all the services running on it have to manage their trade-off between consumption and reliability to prevent a premature depletion of the phone’s battery. In turn, localization is one of the most demanding services in terms of resource consumption. In this dissertation I present an efficient localization solution that includes, in addition to the standard location tracking techniques, the support of other technologies already available on smartphones (e.g., embedded sensors), as well as the integration of both Human Mobility Modelling (HMM) and Machine Learning (ML) techniques. The main goal of the proposed solution is the provision of a continuous tracking service while achieving a sizeable reduction of the energy impact of the localization with respect to standard solutions, as well as the preservation of user privacy by avoiding the use of a back-end server. This results in a Smart Localization Service (SLS), which outperforms current solutions implemented on smartphones in terms of energy consumption (and, therefore, mobile device lifetime), availability of location information, and network traffic volume

A systematic review of crime facilitated by the consumer Internet of Things

The nature of crime is changing — estimates suggest that at least half of all crime is now committed online. Once everyday objects (e.g. televisions, baby monitors, door locks) that are now internet connected, collectively referred to as the Internet of Things (IoT), have the potential to transform society, but this increase in connectivity may generate new crime opportunities. Here, we conducted a systematic review to inform understanding of these risks. We identify a number of high-level mechanisms through which offenders may exploit the consumer IoT including profiling, physical access control and the control of device audio/visual outputs. The types of crimes identified that could be facilitated by the IoT were wide ranging and included burglary, stalking, and sex crimes through to state level crimes including political subjugation. Our review suggests that the IoT presents substantial new opportunities for offending and intervention is needed now to prevent an IoT crime harvest

A Human-Centric Approach to Group-Based Context-Awareness

The emerging need for qualitative approaches in context-aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modeling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendability remains limited. In this paper, we present f-Context as a service-based context-awareness framework, based on language-action perspective (LAP) theory for modeling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems

Enhancing Energy Efficiency and Privacy Protection of Smart Devices

Smart devices are experiencing rapid development and great popularity. Various smart products available nowadays have largely enriched people’s lives. While users are enjoying their smart devices, there are two major user concerns: energy efficiency and privacy protection. In this dissertation, we propose solutions to enhance energy efficiency and privacy protection on smart devices. First, we study different ways to handle WiFi broadcast frames during smartphone suspend mode. We reveal the dilemma of existing methods: either receive all of them suffering high power consumption, or receive none of them sacrificing functionalities. to address the dilemma, we propose Software Broadcast Filter (SBF). SBF is smarter than the “receive-none” method as it only blocks useless broadcast frames and does not impair application functionalities. SBF is also more energy efficient than the “receive-all” method. Our trace driven evaluation shows that SBF saves up to 49.9% energy consumption compared to the “receive-all” method. Second, we design a system, namely HIDE, to further reduce smartphone energy wasted on useless WiFi broadcast frames. With the HIDE system, smartphones in suspend mode do not receive useless broadcast frames or wake up to process use- less broadcast frames. Our trace-driven simulation shows that the HIDE system saves 34%-75% energy for the Nexus One phone when 10% of the broadcast frames are useful to the smartphone. Our overhead analysis demonstrates that the HIDE system has negligible impact on network capacity and packet round-trip time. Third, to better protect user privacy, we propose a continuous and non-invasive authentication system for wearable glasses, namely GlassGuard. GlassGuard discriminates the owner and an imposter with biometric features from touch gestures and voice commands, which are all available during normal user interactions. With data collected from 32 users on Google Glass, we show that GlassGuard achieves a 99% detection rate and a 0.5% false alarm rate after 3.5 user events on average when all types of user events are available with equal probability. Under five typical usage scenarios, the system has a detection rate above 93% and a false alarm rate below 3% after less than 5 user events

Runtime reconfiguration of physical and virtual pervasive systems

Today, almost everyone comes in contact with smart environments during their everyday’s life. Environments such as smart homes, smart offices, or pervasive classrooms contain a plethora of heterogeneous connected devices and provide diverse services to users. The main goal of such smart environments is to support users during their daily chores and simplify the interaction with the technology. Pervasive Middlewares can be used for a seamless communication between all available devices and by integrating them directly into the environment. Only a few years ago, a user entering a meeting room had to set up, for example, the projector and connect a computer manually or teachers had to distribute files via mail. With the rise of smart environments these tasks can be automated by the system, e.g., upon entering a room, the smartphone automatically connects to a display and the presentation starts. Besides all the advantages of smart environments, they also bring up two major problems. First, while the built-in automatic adaptation of many smart environments is often able to adjust the system in a helpful way, there are situations where the user has something different in mind. In such cases, it can be challenging for unexperienced users to configure the system to their needs. Second, while users are getting increasingly mobile, they still want to use the systems they are accustomed to. As an example, an employee on a business trip wants to join a meeting taking place in a smart meeting room. Thus, smart environments need to be accessible remotely and should provide all users with the same functionalities and user experience. For these reasons, this thesis presents the PerFlow system consisting of three parts. First, the PerFlow Middleware which allows the reconfiguration of a pervasive system during runtime. Second, with the PerFlow Tool unexperi- enced end users are able to create new configurations without having previous knowledge in programming distributed systems. Therefore, a specialized visual scripting language is designed, which allows the creation of rules for the commu- nication between different devices. Third, to offer remote participants the same user experience, the PerFlow Virtual Extension allows the implementation of pervasive applications for virtual environments. After introducing the design for the PerFlow system, the implementation details and an evaluation of the developed prototype is outlined. The evaluation discusses the usability of the system in a real world scenario and the performance implications of the middle- ware evaluated in our own pervasive learning environment, the PerLE testbed. Further, a two stage user study is introduced to analyze the ease of use and the usefulness of the visual scripting tool