Quality of Context in Context-Aware Systems

Context-aware Systems (CASs) are becoming increasingly popular and can be found in the areas of wearable computing, mobile computing, robotics, adaptive and intelligent user interfaces. Sensors are the corner stone of context capturing however, sensed context data are commonly prone to imperfection due to the technical limitations of sensors, their availability, dysfunction, and highly dynamic nature of environment. Consequently, sensed context data might be imprecise, erroneous, conflicting, or simply missing. To limit the impact of context imperfection on the behavior of a context-aware system, a notion of Quality of Context (QoC) is used to measure quality of any information that is used as context information. Adaptation is performed only if the context data used in the decision-making has an appropriate quality level. This paper reports an analytical review for state of the art quality of context in context-aware systems and points to future research directions

State of the Art, Trends and Future of Bluetooth Low Energy, Near Field Communication and Visible Light Communication in the Development of Smart Cities

The current social impact of new technologies has produced major changes in all areas of society, creating the concept of a smart city supported by an electronic infrastructure, telecommunications and information technology. This paper presents a review of Bluetooth Low Energy (BLE), Near Field Communication (NFC) and Visible Light Communication (VLC) and their use and influence within different areas of the development of the smart city. The document also presents a review of Big Data Solutions for the management of information and the extraction of knowledge in an environment where things are connected by an “Internet of Things” (IoT) network. Lastly, we present how these technologies can be combined together to benefit the development of the smart city

A Model-driven Architecture for Multi-protocol OBD Emulator

The Internet of Things (IoT) might be the next revolutionary technology to mark a generation. It could have a particularly strong influence on the automotive industry, changing people’s perception of what a vehicle can do. By connecting several things in a car, IoT empowers it to sense and communicate. Furthermore, this technology clearly opens the way to emerging applications such as automated driving, Vehicle-to-Vehicle and Vehicleto-Infrastructure communication. Vehicle’s information about its environment and surroundings is crucial to the development of existing and emerging applications. It is already possible to communicate directly (on-site) with vehicles through a built-in On Board Diagnostics (OBD), making it possible to obtain crucial information about the state of the vehicle in real environments. However, there is zero tolerance for error when developing new applications for vehicles that are, a priori, extremely costly and that must also safeguard human lives. Therefore, there is an increasing need for OBD emulators which can allow the development of new applications. This Thesis proposes a model-driven architecture for multi-protocol OBD emulator, encouraging the development of new emerging OBD systems in a safety environment, to promote the creation of applications to interact or use vehicles’ data. In this sense, the addressed specifications are: Less expensive comparing with today’s solutions; Compatible with different OBD protocols communication; Open Source Hardware and Software suitable for Do-It-Yourself (DIY) development

Mobile-based online data mining : outdoor activity recognition

One of the unique features of mobile applications is the context awareness. The mobility and power afforded by smartphones allow users to interact more directly and constantly with the external world more than ever before. The emerging capabilities of smartphones are fueling a rise in the use of mobile phones as input devices for a great range of application fields; one of these fields is the activity recognition. In pervasive computing, activity recognition has a significant weight because it can be applied to many real-life, human-centric problems. This important role allows providing services to various application domains ranging from real-time traffic monitoring to fitness monitoring, social networking, marketing and healthcare. However, one of the major problems that can shatter any mobile-based activity recognition model is the limited battery life. It represents a big hurdle for the quality and the continuity of the service. Indeed, excessive power consumption may become a major obstacle to broader acceptance context-aware mobile applications, no matter how useful the proposed service may be. We present during this thesis a novel unsupervised battery-aware approach to online recognize users’ outdoor activities without depleting the mobile resources. We succeed in associating the places visited by individuals during their movements to meaningful human activities. Our approach includes novel models that incrementally cluster users’ movements into different types of activities without any massive use of historical records. To optimize battery consumption, our approach behaves variably according to users’ behaviors and the remaining battery level. Moreover, we propose to learn users’ habits in order to reduce the activity recognition computation. Our innovative battery-friendly method combines activity recognition and prediction in order to recognize users’ activities accurately without draining the battery of their phones. We show that our approach reduces significantly the battery consumption while keeping the same high accuracy. Une des caractéristiques uniques des applications mobiles est la sensibilité au contexte. La mobilité et la puissance de calcul offertes par les smartphones permettent aux utilisateurs d’interagir plus directement et en permanence avec le monde extérieur. Ces capacités émergentes ont pu alimenter plusieurs champs d’applications comme le domaine de la reconnaissance d’activités. Dans le domaine de l'informatique omniprésente, la reconnaissance des activités humaines reçoit une attention particulière grâce à son implication profonde dans plusieurs problématiques de vie quotidienne. Ainsi, ce domaine est devenu une pièce majeure qui fournit des services à un large éventail de domaines comme la surveillance du trafic en temps réel, les réseaux sociaux, le marketing et la santé. Cependant, l'un des principaux problèmes qui peuvent compromettre un modèle de reconnaissance d’activité sur les smartphones est la durée de vie limitée de la batterie. Ce handicap représente un grand obstacle pour la qualité et la continuité du service. En effet, la consommation d'énergie excessive peut devenir un obstacle majeur aux applications sensibles au contexte, peu importe à quel point ce service est utile. Nous présentons dans de cette thèse une nouvelle approche non supervisée qui permet la détection incrémentale des activités externes sans épuiser les ressources du téléphone. Nous parvenons à associer efficacement les lieux visités par des individus lors de leurs déplacements à des activités humaines significatives. Notre approche comprend de nouveaux modèles de classification en ligne des activités humaines sans une utilisation massive des données historiques. Pour optimiser la consommation de la batterie, notre approche se comporte de façon variable selon les comportements des utilisateurs et le niveau de la batterie restant. De plus, nous proposons d'apprendre les habitudes des utilisateurs afin de réduire la complexité de l’algorithme de reconnaissance d'activités. Pour se faire, notre méthode combine la reconnaissance d’activités et la prédiction des prochaines activités afin d’atteindre une consommation raisonnable des ressources du téléphone. Nous montrons que notre proposition réduit remarquablement la consommation de la batterie tout en gardant un taux de précision élevé

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

none5noNatural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.openEsposito M.; Palma L.; Belli A.; Sabbatini L.; Pierleoni P.Esposito, M.; Palma, L.; Belli, A.; Sabbatini, L.; Pierleoni, P

Service Oriented Mobile Computing

La diffusione di concetti quali Pervasive e Mobile Computing introduce nell'ambito dei sistemi distribuiti due aspetti fondamentali: la mobilità dell'utente e l'interazione con l'ambiente circostante, favorite anche dal crescente utilizzo di dispositivi mobili dotati di connettività wireless come prodotti di consumo. Per estendere le funzionalità introdotte nell'ambito dei sistemi distribuiti dalle Architetture Orientate ai Servizi (SOA) e dal paradigma peer-to-peer anche a dispositivi dalle risorse limitate (in termini di capacità computazionale, memoria e batteria), è necessario disporre di un middleware leggero e progettato tenendo in considerazione tali caratteristiche. In questa tesi viene presentato NAM (Networked Autonomic Machine), un formalismo che descrive in modo esaustivo un sistema di questo tipo; si tratta di un modello teorico per la definizione di entità hardware e software in grado di condividere le proprie risorse in modo completamente altruistico. In particolare, il lavoro si concentra sulla definizione e gestione di un determinato tipo di risorse, i servizi, che possono essere offerti ed utilizzati da dispositivi mobili, mediante meccanismi di composizione e migrazione. NSAM (Networked Service-oriented Autonomic Machine) è una specializzazione di NAM per la condivisione di servizi in una rete peer-to-peer, ed è basato su tre concetti fondamentali: schemi di overlay, composizione dinamica di servizi e auto-configurazione dei peer. Nella tesi vengono presentate anche diverse attività applicative, che fanno riferimento all'utilizzo di due middleware sviluppati dal gruppo di Sistemi Distribuiti (DSG) dell'Università di Parma: SP2A (Service Oriented Peer-to-peer Architecture), framework per lo sviluppo di applicazioni distribuite attraverso la condivisione di risorse in una rete peer-to-peer, e Jxta-Soap che consente la condivisione di Web Services in una rete peer-to-peer JXTA. Le applicazioni realizzate spaziano dall'ambito della logistica, alla creazione di comunità per l'e-learning, all'Ambient Intelligence alla gestione delle emergenze, ed hanno come denominatore comune la creazione di reti eterogenee e la condivisione di risorse anche tra dispositivi mobili. Viene inoltre messo in evidenza come tali applicazioni possano essere ottimizzate mediante l'introduzione del framework NAM descritto, per consentire la condivisione di diversi tipi di risorse in modo efficiente e proattivo

A Lightweight Attribute-Based Access Control System for IoT.

The evolution of the Internet of things (IoT) has made a significant impact on our daily and professional life. Home and office automation are now even easier with the implementation of IoT. Multiple sensors are connected to monitor the production line, or to control an unmanned environment is now a reality. Sensors are now smart enough to sense an environment and also communicate over the Internet. That is why, implementing an IoT system within the production line, hospitals, office space, or at home could be beneficial as a human can interact over the Internet at any time to know the environment. 61% of International Data Corporation (IDC) surveyed organizations are actively pursuing IoT initiatives, and 6.8% of the average IT budgets is also being allocated to IoT initiatives. However, the security risks are still unknown, and 34% of respondents pointed out that data safety is their primary concern [1]. IoT sensors are being open to the users with portable/mobile devices. These mobile devices have enough computational power and make it di cult to track down who is using the data or resources. That is why this research focuses on proposing a dynamic access control system for portable devices in IoT environment. The proposed architecture evaluates user context information from mobile devices and calculates trust value by matching with de ned policies to mitigate IoT risks. The cloud application acts as a trust module or gatekeeper that provides the authorization access to READ, WRITE, and control the IoT sensor. The goal of this thesis is to offer an access control system that is dynamic, flexible, and lightweight. This proposed access control architecture can secure IoT sensors as well as protect sensor data. A prototype of the working model of the cloud, mobile application, and sensors is developed to prove the concept and evaluated against automated generated web requests to measure the response time and performance overhead. The results show that the proposed system requires less interaction time than the state-of-the-art methods