Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Ontologies for Industry 4.0

The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.Peer ReviewedPostprint (published version

Interoperability middleware for IIoT gateways based on international standard ontologies and standardized digital representation

Recent advances in the areas of microelectronics, information technology, and communication protocols have made the development of smaller devices with greater processing capacity and lower energy consumption. This context contributed to the growing number of physical devices in industrial environments which are interconnected and communicate via the internet, enabling concepts such as Industry 4.0 and the Industrial Internet of Things (IIoT). These nodes have different sensors and actuators that monitor and control environment data. Several companies develop these devices, including diverse communication protocols, data structures, and IoT platforms, which leads to interoperability issues. In IoT scenarios, interoperability is the ability of two systems to communicate and share services. Therefore, communication problems can make it unfeasible to use heterogeneous devices, increasing the project’s financial cost and development time. In an industry, interoperability is related to different aspects, such as physical communication, divergent device communication protocols, and syntactical problems, referring to the distinct data structure. Developing a new standard for solving these matters may bring interoperability-related drawbacks rather than effectively solving these issues. Therefore, to mitigate interoperability problems in industrial applications, this work proposes the development of an interoperability middleware for Edge-enabled IIoT gateways based on international standards. The middleware is responsible for translating communication protocols, updating data from simulations or physical nodes to the assets’ digital representations, and storing data locally or remotely. The middleware adopts the IEEE industrial standard ontologies combined with assets’ standardized digital models. As a case study, a simulation replicates the production of a nutrient solution for agriculture, controlled by IIoT nodes. The use case consists of three devices, each equipped with at least five sensors or actuators, communicating in different communication protocols and exchanging data using diverse structures. The performance of the proposed middleware and its proposed translations algorithms were evaluated, obtaining satisfactory results for mitigating interoperable in industrial applications.Devido a recentes avanços nas áreas de microeletrônica, tecnologia da informação, e protocolos de comunicação tornaram possível o desenvolvimento de dispositivos cada vez menores com maior capacidade de processamento e menor consumo energético. Esse contexto contribuiu para o crescente nú- mero desses dispositivos na industria que estão interligados via internet, viabilizando conceitos como Indústria 4.0 e Internet das Coisas Industrial (IIoT). Esses nós possuem diferentes sensores e atuadores que monitoram e controlam os dados do ambiente. Esses equipamentos são desenvolvidos por diferentes empresas, incluindo protocolos de comunicação, estruturas de dados e plataformas de IoT distintos, acarretando em problemas de interoperabilidade. Em cenários de IoT, interoperabilidade, é a capacidade de sistemas se comunicarem e compartilharem serviços. Portanto, esses problemas podem inviabilizar o uso de dispositivos heterogêneos, aumentando o custo financeiro do projeto e seu tempo de desenvolvimento. Na indústria, interoperabilidade se divide em diferentes aspectos, como comunicação e problemas sintáticos, referentes à estrutura de dados distinta. O desenvolvimento de um padrão industrial pode trazer mais desvantagens relacionadas à interoperabilidade, em vez de resolver esses problemas. Portanto, para mitigar problemas relacionados a intoperabilidade industrial, este trabalho propõe o desenvolvimento de um middleware de interoperável para gateways IIoT baseado em padrões internacionais e ontologias. O middleware é responsável por traduzir diferentes protocolos de comunicação, atualizar os dados dos ativos industriais por meio de suas representações digitais, esses armazenados localmente ou remotamente. O middleware adota os padrões ontológicos industriais da IEEE combinadas com modelos digitais padronizados de ativos industriais. Como estudo de caso, são realizadas simulações para a produção de uma solução nutritiva para agricultura, controlada por nós IIoT. O processo utiliza três dispositivos, cada um equipado com pelo menos cinco sensores ou atuadores, por meio de diferentes protocolos de comunicação e estruturas de dados. O desempenho do middleware proposto e seus algoritmos de tradução foram avaliados e apresentados no final do trabalho, os quais resultados foram satisfatórios para mitigar a interoperabilidade em aplicações industriais

Role-Based data visualization for Industrial IoT

The competition among manufacturers in the global markets calls for the enhancement of the agility and performance of the production process and the quality of products. As a result, the production systems should be designed in a way to provide decision-makers with visibility and analytics. To fulfill these objectives, the development of factory information systems in manufacturing industries has been introduced as a practical solution in the past few years. On the other hand, the volume of data generated on the factory floor is rising. To improve the efficiency of manufacturing process, this amount of data should be analyzed by decision-makers. To cope with this challenge, visualization assists decision-makers to gain insight into data. To give a better perspective of collected data to decision-makers, effective visualization techniques should be employed. Adequate data visualization allows the end user to have better understanding of data and make effective decisions faster. Meanwhile, the adoption of the Service-Oriented Architecture (SOA) and Internet of Things (IoT) as state-of-the-art technologies are among the most prominent trends in industrial automation. IoT technology is expected to generate and collect data from various sensors and devices within the production system, and enables enterprises to have real-time visibility into the flow of production process. Moreover, data received from factory floor should be transmitted from back-end side to the front-end side for future analysis. To implement the exchange of data efficiently, the solution should support different communication protocols to make interoperability among heterogeneous devices on shop floor. This study describes an approach for building a role-based visualization of industrial IoT. An extensible architecture was provided by which the future growth of data and emerging new protocols has been anticipated. By using the IoT platform introduced in this thesis, selected KPIs can be monitored by different levels of enterprise. Three prototype IoT dashboards have been implemented for a pilot production line, “Festo didactic training line” located in Seinäjoki University of Applied Sciences (SeAMK) and results have been validated

Understanding the new Context of Uncertainty and Risk under the 4th Industry Revolution

Proceedings of the 29th European Safety and Reliability Conference (ESREL), 22 – 26 September 2019, Hannover, Germany. Editors, Michael Beer and Enrico ZioThe revolution towards the Industry 4.0, requires as a fundamental challenge the advanced treatment of risk in physical assets according to this new context. This revolution also includes the transition towards a new concept of assets and production systems giving rise to those known as cyber-physical systems (CPS) where the available information and knowledge about the systems and its behaviour should promote a level of control of the risk not known until now. In this context, the transition from classical model for risk management to other concepts, more flexible and dynamic is needed. It is the context that this paper is intended to illustrate, approaching risk control to the available data and technology.Gobierno de España. FFI2017- 89639-P, “Mechanisms in the sciences: from the biological to the social

D5.2: Digital-Twin Enabled multi-physics simulation and model matching

This deliverable presents a report on the developed actions and results concerning Digital-Twin-enabled multi-physics simulations and model matching. Enabling meaningful simulations within new human-infrastructure interfaces such as Digital twins is paramount. Accessing the power of simulation opens manifold new ways for observation, understanding, analysis and prediction of numerous scenarios to which the asset may be faced. As a result, managers can access countless ways of acquiring synthetic data for eventually taking better, more informed decisions. The tool MatchFEM is conceived as a fundamental part of this endeavour. From a broad perspective, the tool is aimed at contextualizing information between multi-physics simulations and vaster information constructs such as digital twins. 3D geometries, measurements, simulations, and asset management coexist in such information constructs. This report provides guidance for the generation of comprehensive adequate initial conditions of the assets to be used during their life span using a DT basis. From a more specific focus, this deliverable presents a set of exemplary recommendations for the development of DT-enabled load tests of assets in the form of a white paper. The deliverable also belongs to a vaster suit of documents encountered in WP5 of the Ashvin project in which measurements, models and assessments are described thoroughly.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Ontology-based context modeling in physical asset integrity management

Asset management is concerned with the management practices, technologies and tools necessary to maximize the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximized too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modeling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, 2-fold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications

An IoT architecture for decision support system in precision livestock

Sustainable animal production is a primary goal of technological development in the livestock industry. However, it is crucial to master the livestock environment due to the susceptibility of animals to variables such as temperature and humidity, which can cause illness, production losses, and discomfort. Thus, livestock production systems require monitoring, reasoning, and mitigating unwanted conditions with automated actions. The principal contribution of this study is the introduction of a self-adaptive architecture named e-Livestock to handle animal production decisions. Two case studies were conducted involving a system derived from the e-Livestock architecture, encompassing a Compost Barn production system - an environment and technology where bovine milk production occurs. The outcomes demonstrate the effectiveness of e-Livestock in three key aspects: (i) abstraction of disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and their incorporation into a single architecture specific to the livestock domain, (ii) support for the reuse and derivation of an adaptive self-architecture to support the engineering of a decision support system for the livestock subdomain, and (iii) support for empirical studies in a real smart farm to facilitate future technology transfer to the industry. Therefore, our research’s main contribution is developing an architecture combining machine learning techniques and ontology to support more complex decisions when considering a large volume of data generated on farms. The results revealed that the e-Livestock architecture could support monitoring, reasoning, forecasting, and automated actions in a milk production/Compost Barn environment.Na indústria pecuária, a produção animal sustentável é o principal objetivo do desenvolvimento tecnológico. Porém, é fundamental manter boas condições no ambiente devido à suscetibilidade dos animais a variáveis como temperatura e umidade, que podem causar doenças, perdas de produção e desconforto. Assim, os sistemas de produção pecuária requerem monitoramento, controle e mitigação das condições indesejadas através de ações automatizadas. A principal contribuição deste estudo é a introdução de uma arquitetura auto-adaptativa denominada e-Livestock para apoiar as decisões relacionadas à produção animal. Foram conduzidos dois estudos de caso, envolvendo a arquitetura e-Livestock, que foi utilizada no sistema de produção Compost Barn - ambiente e tecnologia onde ocorre a produção de gado leiteiro. Os resultados demonstraram a utilidade do e-Livestock para avaliar três aspectos principais: (i) abstração de tecnologias disruptivas baseadas em Internet das Coisas (IoT) e Inteligência Artificial, e sua incorporação em uma arquitetura única, específica para o domínio da pecuária, (ii) suporte para a reutilização e derivação de uma arquitetura auto-adaptativa para apoiar o desenvolvimento de uma aplicação de apoio à decisão para o subdomínio da pecuária e (iii) suporte para estudos empíricos em uma fazenda inteligente real para facilitar a transferência de tecnologia para a indústria. Portanto, a principal contribuição dessa pesquisa é o desenvolvimento de uma arquitetura combinando técnicas de machine learning e ontologia para apoiar decisões mais complexas ao considerar um grande volume de dados gerados nas fazendas. Os resultados revelaram que a arquitetura e-Livestock pode apoiar monitoramento, controle, previsão e ações automatizadas em um ambiente de produção de leite/Compost Barn.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio