Edge/Fog Computing Technologies for IoT Infrastructure

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies

La programación reactiva y de actor en entornos de Internet de las Cosas

El surgimiento de Internet de las Cosas, la Computación Móvil y la rápida adopción de Cloud Computing han cambiado el panorama que presenta la computación distribuida. Estos sistemas distribuidos masivos generarán una cantidad excesiva de datos, que saturarán a las redes. Llevar servicios de red, cómputo y otras funcionalidades al extremo medio (fog) o final (mist) permitirá el uso eficiente de estos recursos, para mantener la continuidad fluida desde el Edge al Cloud. Además, IoT presenta características singulares y los desarrollos actualmente se realizan con herramientas y lenguajes de programación convencionales no adecuados para este tipo de sistemas. Estos lenguajes en su mayoría son inadecuados para desarrollar aplicaciones altamente distribuidas, debido a que no soportan mensajes asincrónicos y los errores se pueden propagar, entre otras limitaciones. Por otro lado la programación reactiva y el modelo de programación de actor proveen recursos adecuados para el desarrollo de soluciones para IoT. La presente propuesta de investigación analizará y evaluará las ventajas de la programación de actor y la programación reactiva, aplicándolas a una variedad de entornos de IoT que favorezcan la continuidad del Edge al Cloud.Eje: Innovación en Sistemas de Software.Red de Universidades con Carreras en Informátic

La programación reactiva y de actor en entornos de Internet de las Cosas

El surgimiento de Internet de las Cosas, la Computación Móvil y la rápida adopción de Cloud Computing han cambiado el panorama que presenta la computación distribuida. Estos sistemas distribuidos masivos generarán una cantidad excesiva de datos, que saturarán a las redes. Llevar servicios de red, cómputo y otras funcionalidades al extremo medio (fog) o final (mist) permitirá el uso eficiente de estos recursos, para mantener la continuidad fluida desde el Edge al Cloud. Además, IoT presenta características singulares y los desarrollos actualmente se realizan con herramientas y lenguajes de programación convencionales no adecuados para este tipo de sistemas. Estos lenguajes en su mayoría son inadecuados para desarrollar aplicaciones altamente distribuidas, debido a que no soportan mensajes asincrónicos y los errores se pueden propagar, entre otras limitaciones. Por otro lado la programación reactiva y el modelo de programación de actor proveen recursos adecuados para el desarrollo de soluciones para IoT. La presente propuesta de investigación analizará y evaluará las ventajas de la programación de actor y la programación reactiva, aplicándolas a una variedad de entornos de IoT que favorezcan la continuidad del Edge al Cloud.Eje: Innovación en Sistemas de Software.Red de Universidades con Carreras en Informátic

La programación reactiva y de actor en entornos de Internet de las Cosas

El surgimiento de Internet de las Cosas, la Computación Móvil y la rápida adopción de Cloud Computing han cambiado el panorama que presenta la computación distribuida. Estos sistemas distribuidos masivos generarán una cantidad excesiva de datos, que saturarán a las redes. Llevar servicios de red, cómputo y otras funcionalidades al extremo medio (fog) o final (mist) permitirá el uso eficiente de estos recursos, para mantener la continuidad fluida desde el Edge al Cloud. Además, IoT presenta características singulares y los desarrollos actualmente se realizan con herramientas y lenguajes de programación convencionales no adecuados para este tipo de sistemas. Estos lenguajes en su mayoría son inadecuados para desarrollar aplicaciones altamente distribuidas, debido a que no soportan mensajes asincrónicos y los errores se pueden propagar, entre otras limitaciones. Por otro lado la programación reactiva y el modelo de programación de actor proveen recursos adecuados para el desarrollo de soluciones para IoT. La presente propuesta de investigación analizará y evaluará las ventajas de la programación de actor y la programación reactiva, aplicándolas a una variedad de entornos de IoT que favorezcan la continuidad del Edge al Cloud.Eje: Innovación en Sistemas de Software.Red de Universidades con Carreras en Informátic

Security risk assessment in Internet of Things systems

Information security risk assessment methods have served us well over the past two decades. They have provided a tool for organizations and governments to use in protecting themselves against pertinent risks. As the complexity, pervasiveness, and automation of technology systems increases and cyberspace matures, particularly with the Internet of Things (IoT), there is a strong argument that we will need new approaches to assess risk and build trust. The challenge with simply extending existing assessment methodologies to IoT systems is that we could be blind to new risks arising in such ecosystems. These risks could be related to the high degrees of connectivity present or the coupling of digital, cyber-physical, and social systems. This article makes the case for new methodologies to assess risk in this context that consider the dynamics and uniqueness of the IoT while maintaining the rigor of best practice in risk assessment

Semantic Driven Approach for Rapid Application Development in Industrial Internet of Things

The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema.org semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated.We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper.Die Entwicklung des Internet der Dinge (IoT) hat die industrielle Automatisierung revolutioniert. Industrielle Geräte auf allen Ebenen wie Feldgeräte, Steuergeräte, Geräte auf Unternehmensebene usw. sind mit dem Internet verbunden, wodurch problemlos auf sie zugegriffen werden kann. Es hat die Art und Weise, wie Anwendungen auf industriellen Automatisierungssystemen entwickelt werden, deutlich verändert. Es führte zum Paradigmenwechsel, wo neuartige IoT Anwendungsentwicklungstools, wie Node-RED, verwendet werden können, um komplexe industrielle Anwendungen als IoT-Orchestrierungen zu entwickeln. Aktuell sind diese Anwendungen jedoch ausschließlich an Geräte bestimmter Anbieter und Ökosysteme gebunden. Sie können nicht mit Geräten anderer Anbieter und Plattformen verbunden werden, da die Anwendungen nicht semantisch interoperabel sind. Daher ist es wünschenswert, plattformunabhängige, herstellerneutrale Anwendungsvorlagen für allgemeine Automatisierungsaufgaben zu verwenden. Im aktuellen Status von Node-RED können solche wiederverwendbaren und interoperablen Anwendungsvorlagen jedoch nicht entwickelt werden. Diese Interoperabilitätsprobleme auf Datenebene können im IoT mithilfe von Semantic Web (SW) -Technologien behoben werden. Für Ingenieure oder Entwickler von IoT-Anwendungen sind SW-Technologien nicht sehr einfach zu verwenden. Zur Erstellung interoperabler IoT-Anwendungen sind daher neuartige IoT-Tools erforderlich. Zu diesem Zweck schlagen wir eine neuartige semantische Erweiterung des weit verbreiteten Node-RED-Tools vor, indem wir semantische Definitionen wie iot.schema.org in die semantischen Modelle von NODE-Red einführen. Das Tool leitet einen Gerätehersteller oder Maschinebauer, die keine Experten in semantische Technologien sind, an um die Semantik eines Geräts konsistent zu konfigurieren. Darüber hinaus ermöglicht es auch einem Ingenieur oder IoT-Anwendungsentwickler, semantische, interoperable IoT-Anwendungen mit minimalem Aufwand zu entwerfen und entwicklen Unser Ansatz beschleunigt die Anwendungsentwicklungsprozesse durch Einführung neuartiger semantischer Anwendungsvorlagen namens Rezepte für Node-RED. Durch die Verwendung von Rezepten können komplexe Anwendungsentwicklungsaufgaben wie das Abgleichen von Funktionen zwischen Rezepten und vorhandenen Strukturen automatisiert werden. Wir demonstrieren Skill-Matching in der Cloud oder am Industrial Edge eines Automatisierungssystems. Wir haben dafür quantitative und qualitative Bewertung unseres Ansatzes durchgeführt, um die Machbarkeit und Skalierbarkeit des Ansatzes in realen Szenarien zu testen. Die Ergebnisse der Bewertung werden in dieser Arbeit vorgestellt und diskutiert

IoT Applications in Energy Supply Systems and Traffic

Internet of Things is a rapidly growing trend in the technology sector. Its working principle is to interconnect devices collecting data from our surroundings, such as sensors, storing large amounts of data, analyse the data and making better and more accurate decisions based on gathered data. IoT can become to play a crucial role in the energy and transport sector that strive towards increasing sustainability through effective use of resources. As the technology required decreases in cost, it becomes more beneficial to monitor and interconnect more devices, leading to a better data of whole ecosystems, such as a power plant supply chain. This thesis describes some IoT applications for the energy and traffic industry, introduces a reference model for planning IoT applications and analyses four case studies based on the reference model in order to determine whether the proposed model could be suitable for planning IoT applications. The mentioned case studies were conducted by a project collaboration between Åbo Akademi university and Novia University of Applied Sciences and discovered possible IoT implementations for a remote-area exhibition centre, a detached house, a housing with several apartments and energy consumption in traffic. Each case study is analysed separately and recommendations for further development are given

Méthodes d'Accès au Canal pour les Réseaux Dédiés à l'Internet des Objets

Dedicated networks for the Internet of Things appeared with the promise of connecting thousands of nodes, or even more, to a single base station in a star topology. This new logic represents a fundamental change in the way of thinking about networks, after decades during which research work mainly focused on multi-hop networks.Internet of Things networks are characterized by long transmission range, wide geographic coverage, low energy consumption and low set-up costs. This made it necessary to adapt the protocols at different architectural layers in order to meet the needs of these networks.Several players compete in the Internet of Things market, each trying to establish the most efficient solution. These players are mostly focused on modifying the physical layer, on the hardware part or through proposing new modulations. However, with regard to the channel access control solution (known as the MAC protocol), all the solutions proposed by these players are based on classic approaches such as Aloha and CSMA.The objective of this thesis is to propose a dynamic MAC solution for networks dedicated to the Internet of Things. The proposed solution has the ability to adapt to network conditions. This solution is based on a machine learning algorithm that learns from network history in order to establish the relationship between network conditions, MAC layer parameters and network performance in terms of reliability and energy consumption. The solution also has the originality of making possible the coexistence of nodes using different MAC configurations within the same network. The results of simulations have shown that a MAC solution based on machine learning could take advantage of the good properties of different conventional MAC protocols. The results also show that a cognitive MAC solution always offers the best compromise between reliability and energy consumption, while taking into account the fairness between the nodes of the network. The cognitive MAC solution tested for high density networks has proven better scalability compared to conventional MAC protocols, which is another important advantage of our solution.Les réseaux dédiés pour l’Internet des Objets sont apparus avec la promesse de connecter des milliers de nœuds, voire plus, à une seule station de base dans une topologie en étoile. Cette nouvelle logique représente un changement fondamental dans la façon de penser les réseaux, après des décennies pendant lesquelles les travaux de recherche se sont focalisés sur les réseaux multi-sauts.Les réseaux pour l’Internet des Objets se caractérisent par la longue portée des transmissions, la vaste couverture géographique, une faible consommation d’énergie et un bas coût de mise en place. Cela a rendu nécessaire des adaptations à tous les niveaux protocolaires afin de satisfaire les besoins de ces réseaux.Plusieurs acteurs sont en concurrence sur le marché de l’Internet des Objets, essayant chacun d’établir la solution la plus efficiente. Ces acteurs se sont concentrés sur la modification de la couche physique, soit au niveau de la partie matérielle, soit par la proposition de nouvelles techniques de modulation. Toutefois, en ce qui concerne la solution de contrôle d’accès au canal (connue sous le nom de couche MAC), toutes les solutions proposées par ces acteurs se fondent sur des approches classiques, tel que Aloha et CSMA.L'objectif de cette thèse est de proposer une solution MAC dynamique pour les réseaux dédiés à l’Internet des Objets. La solution proposée a la capacité de s'adapter aux conditions du réseau. Cette solution est basée sur un algorithme d'apprentissage automatique, qui apprend de l'historique du réseau afin d'établir la relation entre les conditions du réseau, les paramètres de la couche MAC et les performances du réseau en termes de fiabilité et de consommation d'énergie. La solution possède également l'originalité de faire coexister des nœuds utilisant de différentes configurations MAC au sein du même réseau. Les résultats de simulations ont montré qu'une solution MAC basée sur l'apprentissage automatique pourrait tirer profit des avantages des différents protocoles MAC classiques. Les résultats montrent aussi qu'une solution MAC cognitive offre toujours le meilleur compromis entre fiabilité et consommation d'énergie, tout en prenant en compte l'équité entre les nœuds du réseau. La solution MAC cognitive testée pour des réseaux à haute densité a prouvé des bonnes propriétés de passage à l’échelle par rapport aux protocoles MACs classiques, ce qui constitue un autre atout important de notre solution