An Architecture to Support the Collection of Big Data in the Internet of Things

International audienceThe Internet of Things (IoT) relies on physical objects interconnected between each others, creating a mesh of devices producing information. In this context, sensors are surrounding our environment (e.g., cars, buildings, smartphones) and continuously collect data about our living environment. Thus, the IoT is a prototypical example of Big Data. The contribution of this paper is to define a software architecture supporting the collection of sensor-based data in the context of the IoT. The architecture goes from the physical dimension of sensors to the storage of data in a cloud-based system. It supports Big Data research effort as its instantiation supports a user while collecting data from the IoT for experimental or production purposes. The results are instantiated and validated on a project named SMARTCAMPUS, which aims to equip the SophiaTech campus with sensors to build innovative applications that supports end-users

Software Development Support for Shared Sensing Infrastructures: A Generative and Dynamic Approach

International audienceSensors networks are the backbone of large sensing infras-tructures such as Smart Cities or Smart Buildings. Classical approaches suffer from several limitations hampering developers' work (e.g., lack of sensor sharing, lack of dynamicity in data collection policies, need to dig inside big data sets, absence of reuse between implementation platforms). This paper presents a tooled approach that tackles these issues. It couples (i) an abstract model of developers' requirements in a given infrastructure to (ii) timed automata and code generation techniques, to support the efficient deployment of reusable data collection policies on different infrastructures. The approach has been validated on several real-world scenarios and is currently experimented on an academic campus

Review submerged speleothems and sea level reconstructions: A global overview and new results from the mediterranean sea

This study presents a global overview of the submerged speleothems used to reconstruct paleo sea levels and reports new results from two stalactites collected in the Mediterranean Sea. Coastal cave deposits significantly contributed to the understanding of global and regional sea-level variations during the Middle and Late Quaternary. The studied speleothems cover the last 1.4 Myr and focused mainly on Marine Isotope Stages (MIS) 1, 2, 3, 5.1, 5.3, 5.5, 7.1, 7.2, 7.3 and 7.5. The results indicate that submerged speleothems represent extraordinary archives that can provide detailed information on former sea-level changes. The two stalactites collected in the central Mediterranean Sea, at Favignana and Ustica islands (Sicily, Italy), are both characterized by continental, phreatic or marine layers. The U-Th and14C ages of the new speleothems provide results of great interest for relative sea-level changes over the last 1000 years

Internet of Things as a Service (iTaaS): challenges and solutions for management of sensor data on the Cloud and the Fog

Building upon cloud, IoT and smart sensors technologies we design and de- velop an IoT as a Service (iTaaS) framework, that transforms a user device (e.g. a smart phone) to an IoT gateway that allows for fast and efficient data streams transmission to the cloud. We develop a two-fold solution, based on micro-services for the IoT (users’ smart devices) and the cloud side (back-end services). iTaaS includes configurations for (a) the IoT side to support data collection from IoT devices to a gateway on a real time basis and, (b) the cloud back-end side to support data sharing, storage and processing. iTaaS provides the technology foreground to enable immediate application deployments in the domain of interest. An obvious and promising implementation of this technology is e-Health and remote health monitoring. As a proof of concept we implement a real time remote patient monitoring system that integrates the proposed frame- work and uses BLE pulse oximeter and heart rate monitoring sensing devices. The experimental analysis shows fast data collection, as (for our experimental setup) data is transmitted from the IoT side (i.e. the gateway) to the cloud in less than 130ms. We also stress the back-end system with high user concurrency (for example with 40 users per second) and high data streams (for example 240 data records per second) and we show that the requests are executed at around 1 second, a number that signifies a satisfactory performance by considering the number of requests, the network latency and the relatively small size of the Virtual Machines implementing services on the cloud (2GB RAM, 1 CPU and 20GB hard disk size)

DEPOSIT : une approche pour exprimer et déployer des politiques de collecte sur des infrastructures de capteurs hétérogènes et partagées

Sensing infrastructures are classically used in the IoT to collect data. However, a deep knowledge of sensing infrastructures is needed to properly interact with the deployed systems. For software engineers, targeting these systems is tedious. First, the specifies of the platforms composing the infrastructure compel them to work with little abstractions and heterogeneous devices. This can lead to code that badly exploit the network infrastructure. Moreover, by being infrastructure specific, these applications cannot be easily reused across different systems. Secondly, the deployment of an application is outside the domain expertise of a software engineer as she needs to identify the required platform(s) to support her application. Lastly, the sensing infrastructure might not be designed to support the concurrent execution of various applications leading to redundant deployments when a new application is contemplated. In this thesis we present an approach that supports (i) the definition of data collection policies at high level of abstraction with a focus on their reuse, (ii) their deployment over a heterogeneous infrastructure driven by models designed by a network export and (iii) the automatic composition of the policy on top of the heterogeneous sensing infrastructures. Based on these contributions, a software engineer can exploit sensor networks without knowing the associated details, while reusing architectural abstractions available off-the-shelf in their policy. The network will also be shared automatically between the policies.Les réseaux de capteurs sont utilisés dans l’IoT pour collecter des données. Cependant, une expertise envers les réseaux de capteurs est requise pour interagir avec ces infrastructures. Pour un ingénieur logiciel, cibler de tels systèmes est difficile. Les spécifications des plateformes composant l'infrastructure de capteurs les obligent à travailler à un bas niveau d'abstraction et à utiliser des plateformes hétérogènes. Cette fastidieuse activité peut conduire à un code exploitant de manière non optimisée l’infrastructure. En étant spécifiques à une infrastructure, ces applications ne peuvent également pas être réutilisées facilement vers d’autres infrastructures. De plus, le déploiement de ces applications est hors du champ de compétences d’un ingénieur logiciel car il doit identifier la ou les plateforme(s) requise(s) pour supporter l’application. Enfin, l’architecture peut ne pas être conçue pour supporter l’exécution simultanée d’application, engendrant des déploiements redondants lorsqu’une nouvelle application est identifiée. Dans cette thèse, nous présentons une approche qui supporte (i) la définition de politiques de collecte de données à haut niveau d’abstraction et réutilisables, (ii) leur déploiement sur une infrastructure hétérogène dirigée par des modèles apportés par des experts réseau et (iii) la composition automatique de politiques sur des infrastructures hétérogènes. De ces contributions, un ingénieur peut dès lors manipuler un réseau de capteurs sans en connaitre les détails, en réutilisant des abstractions architecturales disponibles lors de l'expression des politiques, des politiques qui pourront également coexister au sein d'un même réseau

DEPOSIT : an approach to model and deploy data collection policies on heterogeneous and shared sensor networks

Les réseaux de capteurs sont utilisés dans l’IoT pour collecter des données. Cependant, une expertise envers les réseaux de capteurs est requise pour interagir avec ces infrastructures. Pour un ingénieur logiciel, cibler de tels systèmes est difficile. Les spécifications des plateformes composant l'infrastructure de capteurs les obligent à travailler à un bas niveau d'abstraction et à utiliser des plateformes hétérogènes. Cette fastidieuse activité peut conduire à un code exploitant de manière non optimisée l’infrastructure. En étant spécifiques à une infrastructure, ces applications ne peuvent également pas être réutilisées facilement vers d’autres infrastructures. De plus, le déploiement de ces applications est hors du champ de compétences d’un ingénieur logiciel car il doit identifier la ou les plateforme(s) requise(s) pour supporter l’application. Enfin, l’architecture peut ne pas être conçue pour supporter l’exécution simultanée d’application, engendrant des déploiements redondants lorsqu’une nouvelle application est identifiée. Dans cette thèse, nous présentons une approche qui supporte (i) la définition de politiques de collecte de données à haut niveau d’abstraction et réutilisables, (ii) leur déploiement sur une infrastructure hétérogène dirigée par des modèles apportés par des experts réseau et (iii) la composition automatique de politiques sur des infrastructures hétérogènes. De ces contributions, un ingénieur peut dès lors manipuler un réseau de capteurs sans en connaitre les détails, en réutilisant des abstractions architecturales disponibles lors de l'expression des politiques, des politiques qui pourront également coexister au sein d'un même réseau.Sensing infrastructures are classically used in the IoT to collect data. However, a deep knowledge of sensing infrastructures is needed to properly interact with the deployed systems. For software engineers, targeting these systems is tedious. First, the specifies of the platforms composing the infrastructure compel them to work with little abstractions and heterogeneous devices. This can lead to code that badly exploit the network infrastructure. Moreover, by being infrastructure specific, these applications cannot be easily reused across different systems. Secondly, the deployment of an application is outside the domain expertise of a software engineer as she needs to identify the required platform(s) to support her application. Lastly, the sensing infrastructure might not be designed to support the concurrent execution of various applications leading to redundant deployments when a new application is contemplated. In this thesis we present an approach that supports (i) the definition of data collection policies at high level of abstraction with a focus on their reuse, (ii) their deployment over a heterogeneous infrastructure driven by models designed by a network export and (iii) the automatic composition of the policy on top of the heterogeneous sensing infrastructures. Based on these contributions, a software engineer can exploit sensor networks without knowing the associated details, while reusing architectural abstractions available off-the-shelf in their policy. The network will also be shared automatically between the policies

Implantes dentários unitários realizados com carga imediata em área estética: revisão de literatura

Trabalho de Conclusão de Curso, apresentado para obtenção do grau de Bacharel no curso de Odontologia da Universidade do Extremo Sul Catarinense, UNESC.A literatura recente mostra a utilização de implantes com carga imediata em área estética como uma solução rápida, eficaz e com um bom prognóstico em longo prazo. Além da previsibilidade estética, o conforto do paciente também é levado em questão, no qual, não há necessidade de esperar a osseointegração do implante. O estudo teve como objetivo relatar por meio de revisão da literatura de 2003 á 2015 buscando na base de dados MEDLINE sobre a utilização de implantes unitários com carga imediata em área estética, com ênfase na taxa de sucesso, perda óssea peri-implantar e estética rosa. A carga imediata em implantes demonstra várias vantagens e quando realizado de maneira correta, possui elevada taxa de sucesso comprovando ser um procedimento de escolha para cirurgiões experientes que buscam a satisfação do paciente

Role of the genes Md-ACO1 and Md-ACS1 in ethylene production and shelf life of apple (Malus domestica Borkh)

Shelf life determines the economic life time of mature apples, which can be either freshly harvested or stored. Good shelf life is highly associated with a slow decrease of fruit firmness at room temperature. Apple is a climacteric fruit, in which loss of firmness seems to be physiologically related to ethylene. Ethylenes biosynthetic pathway is controlled by two large gene families coding for 1-aminocyclopropane-1-carboxylate synthase (ACS) and 1-aminocyclopropane-1-carboxylate oxydase (ACO). In this study, one ACS and one ACO gene were examined for their effect on ethylene production and shelf life in apple using gene specific molecular marker, and have also been positioned on a molecular marker linkage map. The ACO marker was developed in this research and mapped on linkage group (LG) 10 of the crosses Prima × Fiesta and Fuji × Mondial Gala, within the 5% border of a previously identified fruit firmness QTL [Theor Appl Genet 100 (2000) 1074]. We denoted this locus as Md-ACO1. In addition, we mapped the previously developed Md-ACS1 marker [Theor Appl Genet 101 (2000) 742] on LG15. Studies on the cross Fuji × Braeburn revealed that Md-ACS1 and Md-ACO1 independently affect the internal ethylene concentration (IEC) as well as shelf life of apple, Md-ACS1 having the strongest effect. Descendants homozygous for Md-ACS1-2 and Md-ACO1-1 showed to have the lowest ethylene production as well as superior shelf-life. These two genes are candidates to be included in marker assisted breedin