A study of existing Ontologies in the IoT-domain

Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of Thing

A reference architecture for federating IoT infrastructures supporting semantic interoperability

: The Internet-of-Things (IoT) is unanimously identified as one of the main pillars of future smart scenarios. However, despite the growing number of IoT deployments, the majority of IoT applications tend to be self-contained, thereby forming vertical silos. Indeed, the ability to combine and synthesize data streams and services from diverse IoT platforms and testbeds, holds the promise to increase the potential of smart applications in terms of size, scope and targeted business context. This paper describes the system architecture for the FIESTA-IoT platform, whose main aim is to federate a large number of testbeds across the planet, in order to offer experimenters the unique experience of dealing with a large number of semantically interoperable data sources. This system architecture was developed by following the Architectural Reference Model (ARM) methodology promoted by the IoT-A project (FP7 “light house” project on Architecture for the Internet of Things). Through this process, the FIESTAIoT architecture is composed of a set of Views that deals with a “logical” functional decomposition (Functional View, FV) and data structuring and annotation, data flows and inter-functional component interactions (Information View, IV)

Business Process Model for IOT Based Systems Operations

The internet of things (IoT) is an innovative and advanced high-level IT development that provides the connection between a large network of devices equipped with numerous computing capabilities, actuation, and sensing with the help of internet connection, consequently providing multifarious novel services regarding smart systems. All around the globe the attractive big data analytics and IoT services are allowing initiatives regarding smart systems. Business processes are commonly executed inside the application systems where computers, objects of IoT as well as humans participate. However, for the system-supported processes, the use of IoT technology is still facing the problem of the absence of a standard system architecture that is essential to manage the coordination in a smart IoT environment. Business process management (BPM) is regarded as a substantial technique for designing, controlling, and improving the processes of a system. This article introduces a BPM modeling approach for IoT-based systems operation exploits IoT using BPM by adopting an IoT framework architecture and considering IoT data for interaction in a defined process model. The methodology has been carried out on top of current BPM modeling notions and system techniques for formal representations of the system and also to get through the challenges of collaboration and connection

Adaptive learning-based resource management strategy in fog-to-cloud

Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C. F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements. So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for identifying and defining the taxonomic model for all the participating devices and system involved services-tasks. In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system. Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed and designed a secure and distributed database framework for effectively and securely distribute the data over the network. To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources. Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version

Sensae Console - Platforma de support para serviços baseados em IoT

Today there are more smart devices than people. The number of devices worldwide is forecast to almost triple from 8.74 billion in 2020 to more than 25.4 billion devices in 2030. The Internet of Things (IoT) is the connection of millions of smart devices and sensors connected to the Internet. These connected devices and sensors collect and share data for use and analysis by many organizations. Some examples of intelligent connected sensors are: GPS asset tracking, parking spots, refrigerator thermostats, soil condition and many others. The limit of different objects that can become intelligent sensors is limited only by our imagination. But these devices are mostly useless without a platform to analyze, store and present the aggregated data into business-oriented information. Recently, several platforms have emerged to address this need and help companies/governments to increase efficiency, cut on operational costs and improve safety. Sadly, most of these platforms are tailor made for the devices that the company offers. This dissertation presents the (Sensae Console) platform that enables and promotes the development of IoT-based business-oriented applications. This platform attempts to be device-neutral, IoT middleware-neutral and provide flexible upstream integration and hosting options while providing a simple and concise data streaming Application Programming Interface (API). Three IoT-based business-oriented applications built on top of the Sensae Console platform are presented as Proof of Concept (PoC) of its capabilities.Atualmente, existem mais sensores inteligentes do que pessoas. O número de sensores em todo o mundo deve quase triplicar de 8,74 bilhões em 2020 para mais de 25,4 bilhões em 2030. O conceito de IoT está relacionado com a interação entre milhões de dispositivos inteligentes através da Internet. Estes dispositivos e sensores conectados recolhem e disponibilizam dados para uso e análise por parte de muitas organizações. Alguns exemplos de sensores inteligentes e seus usos são: dispositivos GPS para rastreamento de ativos, monitorização de vagas de estacionamento, termostatos em arcas frigoríficas, condição do solo e muitos outros. O número de diferentes objetos que podem vir-se a tornar sensores inteligentes é limitado apenas pela nossa imaginação. Mas estes dispositivos são praticamente inúteis sem uma plataforma para analisar, armazenar e apresentar os dados agregados em informação relevante para o negócio em questão. Recentemente, várias plataformas surgiram para responder a essa necessidade e ajudar empresas/governos a aumentar a sua eficiência, reduzir custos operacionais e melhorar a segurança dos espaços e negócios. Infelizmente, a maioria dessas plataformas é feita à medida para os dispositivos que a empresa em questão oferece. Esta tese apresenta uma plataforma (Sensae Console) focada em que propiciar a criação de aplicações relacionados com IoT para negócios específicos. Esta plataforma procura ser agnóstica em relação aos dispositivos inteligentes e middleware de IoT usados por terceiros, oferece variadas e flexíveis opções de integração e hosting como também uma API de streaming simples e concisa. Três aplicações relacionadas com IoT, orientadas ao seu negócio e construídas com base na plataforma Sensae Console são apresentadas como provas de conceito das capacidades da plataforma

Framework for design, simulation and functional prototyping of wearable IoT devices.

El presente proyecto tiene como propósito facilitar el diseño, simulación y prototipado funcional de dispositivos IoT vestibles. Estos dispositivos vestibles son elementos de cómputo con una gran capacidad de interacción con las personas y de comunicación con Internet. Estos dispositivos presentan una oportunidad para los ecosistemas donde se requiere implementar el desarrollo e innovación de base tecnológica, como en Colombia, país que cuenta con políticas encaminadas hacia este horizonte. Sin embargo, el proceso de desarrollo de tales equipos en un ambiente competitivo que se desarrolla a la velocidad de la tecnología de punta se considera complejo debido a factores como el tiempo de desarrollo, la interdisciplinariedad del equipo de trabajo necesario y la necesidad de implementación de funcionalidades avanzadas acordes con el desarrollo tecnológico actual. Para abordar estas dificultades se propuso el framework denominado Frame-WIoT, utilizando un enfoque de diseño basado en modelos con el cual se pudo abordar las dificultades inherentes al desarrollo de dispositivos vestibles. El trabajo consideró diseñar una arquitectura genérica que permita representar los dispositivos vestibles, de acuerdo con la documentación científica. El siguiente paso fue implementar los componentes de la arquitectura en un ambiente de simulación, Simulink, con el objetivo de formalizar el diseño genérico del punto anterior. Finalmente, se generaron los componentes de simulación y prototipado que fueron evaluados con la construcción de un prototipo funcional de dispositivo.LISTA DE FIGURAS 11 LISTA DE ANEXOS 16 RESUMEN 17 ABSTRACT 18 INTRODUCCION 19 1 PROBLEMA, PREGUNTA E HIPOTESIS DE INVESTIGACIÓN 21 1.1 PROBLEMA 21 1.1.1 Pregunta 23 1.1.2 Hipótesis 23 1.2 OBJETIVOS 25 1.2.2 Objetivos específicos 25 1.3 JUSTIFICACIÓN 26 2 MARCO REFERENCIAL 28 2.1 MARCO CONCEPTUAL 28 2.1.1 Framework 28 2.1.2 Diseño 28 2.1.3 Simulación 28 2.1.4 Prototipado 28 2.1.5 Dispositivo vestible 28 2.2 MARCO TEÓRICO 29 2.2.1 Internet de las cosas 29 2.2.2 Modelo de referencia de IoT 29 2.2.3 Capacidades de dispositivo IoT 29 2.2.4 Computación vestible 30 2.2.5 Vestibilidad 31 2.3 ESTADO DEL ARTE 32 2.3.1 Prototipado de vestibles: Aplicaciones y enfoques 33 2.3.2 Frameworks y otras herramientas para el prototipado 37 2.3.3 Consideraciones finales 41 2.4 MARCO LEGAL Y POLÍTICO 43 2.5 MARCO CONTEXTUAL 45 3 ASPECTOS METODOLÓGICOS 46 3.1 ENFOQUE Y TIPO DE INVESTIGACIÓN 46 3.2 TÉCNICAS E INSTRUMENTOS DE RECOLECCIÓN DE INFORMACIÓN 47 3.3 ACTIVIDADES REALIZADAS 48 3.3.1 Diseño de una arquitectura genérica para dispositivos vestibles 48 3.3.2 Implementación de los componentes de la arquitectura propuesta en Simulink 49 3.3.3 Construcción del componente de simulación del framework 50 3.3.4 Construcción del componente de prototipado del framework 51 4 ARQUITECTURA GENÉRICA PARA DISPOSITIVOS IOT VESTIBLES 53 4.1 ANÁLISIS DE ARQUITECTURAS ENCONTRADAS EN LA LITERATURA CIENTÍFICA 53 4.2 REQUISITOS DE UN VESTIBLE 57 4.3 MODELO DE DOMINIO PARA IOT VESTIBLE 60 4.4 FLUJO DE INFORMACIÓN EN LA ARQUITECTURA IOT-A 62 4.4.1 Servicio adquiere valor de un sensor 62 4.4.2 Almacenamiento de información del sensor 62 4.5 FLUJO DE INFORMACIÓN EN EL DISPOSITIVO VESTIBLE 62 4.6 DIAGRAMA DE COMPONENTES 63 4.7 DIAGRAMA DE DESPLIEGUE 65 5 ARQUITECTURA IMPLEMENTADA EN SIMULINK 68 5.1 COMPONENTE DE ADQUISICIÓN 71 5.2 COMPONENTE DE PROCESAMIENTO 74 5.3 COMPONENTE DE ALMACENAMIENTO 76 5.4 COMPONENTE DE SALIDA/CTUACIÓN 77 5.5 COMPONENTE DE COMUNICACIÓN 79 6 ENTORNO DE SIMULACIÓN PARA FRAME-WIOT 81 6.1 ESCENARIOS DE SIMULACIÓN 81 6.1.1 Escenario de interacción con la persona 83 6.1.2 Escenario de comunicación de datos 83 6.2 ELEMENTOS DEL ENTORNO DE SIMULACIÓN PARA FRAME-WIOT 84 6.3 MODELO DE COMPONENTES DE LA ARQUITECTURA DE DISPOSITIVO VESTIBLE EN SIMULINK 84 6.3.1 Componente de adquisición 84 6.3.2 Componente de procesamiento 85 6.3.3 Componente de actuación 87 6.3.4 Componente de comunicación 88 6.3.5 Componente de almacenamiento 89 6.4 INTERFAZ DE SIMULACIÓN 89 6.5 INTERFAZ DE SALIDA DE VIDEO 90 6.6 MODELO DEL CUERPO HUMANO 91 7 ENTORNO DE PROTOTIPADO 94 7.1 RECURSOS PARA LA IMPLEMENTACIÓN DE PROTOTIPOS 94 7.1.1 Raspberry Pi 94 7.1.2 Thingspeak 95 7.1.3 Modelo de prototipado 97 7.2 COMPONENTES MODIFICADOS PARA PROTOTIPADO 99 7.2.1 Componente de adquisición 100 7.2.2 Componente de comunicación 101 7.2.3 Componente de actuación/salida. 102 7.3 PRUEBAS IMPLEMENTADAS 104 7.3.1 Pruebas para el componente de adquisición 104 7.3.2 Pruebas al componente de actuación 105 7.3.3 Pruebas al componente de comunicación 107 7.4 PRUEBA DE CONCEPTO 110 7.4.1 Problema 110 7.4.2. Solución planteada 111 7.4.3 Escenarios evaluados 111 7.4.4 Conclusiones sobre la prueba de concepto 118 8 RESULTADOS 119 9 CONCLUSIONES Y RECOMENDACIONES 121 9.1 CONCLUSIONES 121 9.2 RECOMENDACIONES 125 10 REFERENCIAS 127 11 ANEXOS 140MaestríaThe purpose of this project is to facilitate the design, simulation and functional prototyping of wearable IoT devices. These wearable devices are computational elements with a great capacity for interaction with people and communication with the Internet. These devices present an opportunity for ecosystems where it is necessary to implement technology-based development and innovation, as in Colombia, a country that has policies aimed at this horizon. However, the process of developing such equipment in a competitive environment that develops at the speed of cutting-edge technology is considered complex due to factors such as development time, the interdisciplinary nature of the necessary work team and the need to implement advanced functionalities in line with current technological development. To address these difficulties, the framework called Frame-WIoT was proposed, using a design approach based on models with which the inherent difficulties in the development of wearable devices could be addressed. The work considered to design a generic architecture that allows to represent wearable devices, according to the scientific documentation. The next step was to implement the components of the architecture in a simulation environment, Simulink, with the aim of formalizing the generic design of the previous point. Finally, the simulation and prototyping components that were evaluated with the construction of a functional device prototype were generated

A Reinforcement Learning Quality of Service Negotiation Framework For IoT Middleware

The Internet of Things (IoT) ecosystem is characterised by heterogeneous devices dynamically interacting with each other to perform a specific task, often without human intervention. This interaction typically occurs in a service-oriented manner and is facilitated by an IoT middleware. The service provision paradigm enables the functionalities of IoT devices to be provided as IoT services to perform actuation tasks in critical-safety systems such as autonomous, connected vehicle system and industrial control systems. As IoT systems are increasingly deployed into an environment characterised by continuous changes and uncertainties, there have been growing concerns on how to resolve the Quality of Service (QoS) contentions between heterogeneous devices with conflicting preferences to guarantee the execution of mission-critical actuation tasks. With IoT devices with different QoS constraints as IoT service providers spontaneously interacts with IoT service consumers with varied QoS requirements, it becomes essential to find the best way to establish and manage the QoS agreement in the middleware as a compromise in the QoS could lead to negative consequences. This thesis presents a QoS negotiation framework, IoTQoSystem, for IoT service-oriented middleware. The QoS framework is underpinned by a negotiation process that is modelled as a Markov Decision Process (MDP). A model-based Reinforcement Learning negotiation strategy is proposed for generating an acceptable QoS solution in a dynamic, multilateral and multi-parameter scenarios. A microservice-oriented negotiation architecture is developed that combines negotiation, monitoring and forecasting to provide a self-managing mechanism for ensuring the successful execution of actuation tasks in an IoT environment. Using a case study, the developed QoS negotiation framework was evaluated using real-world data sets with different negotiation scenarios to illustrate its scalability, reliability and performance

Ontology-based context-aware model for event processing in an IoT environment

The Internet of Things (IoT) is more and more becoming one of the fundamental sources of data. The observations produced by these sources are made accessible with heterogeneous vocabularies, models and data formats. The heterogeneity factor in such an enormous environment complicates the task of sharing and reusing this data in a more intelligent way (other than the purposes it was initially set up for). In this research, we investigate these challenges, considering how we can transform raw sensor data into a more meaningful information. This raw data will be modelled using ontology-based information that is accessible through continuous queries for sensor streaming data.Interoperability among heterogeneous entities is an important issue in an IoT environment. Semantic modelling is a key element to support interoperability. Most of the current ontologies for IoT mainly focus on resources and services information. This research builds upon the current state-of-the-art ontologies to provide contextual information and facilitate sensor data querying. In this research, we present an Ontology to represent an IoT environment, with emphasis on temporal and geospatial context enrichment. Furthermore, the Ontology is used alongside a proposed syntax based on Description Logic to build an Event Processing Model. The aim of this model is to interconnect ontology-based reasoning with event processing. This model enables to perform event processing over high-level ontological concepts.The Ontology was developed using the NeOn methodology, which emphasises on the reuse and modularisation. The Competency Questions techniques was used to develop the requirements of this Ontology. This was later evaluated by domain experts in software engineering and cloud computing. The ontology was evaluated based on its completeness, conciseness, consistency and expandability, over 70% of the domain experts agreed on the core modules, concepts and relationships within the ontology. The resulted Ontology provides a core IoT ontology that could be used for further development within a specific IoT domain. IIThe proposed Ontology-Based Context-Aware model for Event-Processing in an IoT environment “OCEM-IoT”, implements all the time operators used in complex event processing engines. Throughput and latency were used as performance comparison metrics for the syntax evaluation; the results obtained show an improved performance over existing event processing languages