Towards interoperability of entity-based and event-based IoT platforms: The case of NGSI and EPCIS standards

With the advancement of IoT devices and thanks to the unprecedented visibility and transparency they provide, diverse IoT-based applications are being developed. With the proliferation of IoT, both the amount and type of data items captured have increased dramatically. The data generated by IoT devices reside in different organizations and systems, and a major barrier to utilizing the data is the lack of interoperability among the standards used to capture the data. To reduce this barrier, two major standards have emerged: the Global Standards One (GS1) Electronic Product Code Information Service (EPCIS) and the FIWARE Next Generation Services Interface (NGSI). However, the two standards differ not only in the data encoding but also in the underlying philosophy of representing IoT data; namely, EPCIS is event-based, and NGSI is entity-based. Interoperability between FIWARE and EPCIS is essential for system integration. This paper presents OLIOT Mediation Gateway, now one of the incubated generic enablers offered by the FIWARE Foundation, that realizes the required interoperability between NGSI and EPCIS systems. It also demonstrates the applicability and feasibility of the Gateway by applying it to a real-life case study of integrating transparency systems used in a meat supply chain

Towards Data Sharing across Decentralized and Federated IoT Data Analytics Platforms

In the past decade the Internet-of-Things concept has overwhelmingly entered all of the fields where data are produced and processed, thus, resulting in a plethora of IoT platforms, typically cloud-based, that centralize data and services management. In this scenario, the development of IoT services in domains such as smart cities, smart industry, e-health, automotive, are possible only for the owner of the IoT deployments or for ad-hoc business one-to-one collaboration agreements. The realization of "smarter" IoT services or even services that are not viable today envisions a complete data sharing with the usage of multiple data sources from multiple parties and the interconnection with other IoT services. In this context, this work studies several aspects of data sharing focusing on Internet-of-Things. We work towards the hyperconnection of IoT services to analyze data that goes beyond the boundaries of a single IoT system. This thesis presents a data analytics platform that: i) treats data analytics processes as services and decouples their management from the data analytics development; ii) decentralizes the data management and the execution of data analytics services between fog, edge and cloud; iii) federates peers of data analytics platforms managed by multiple parties allowing the design to scale into federation of federations; iv) encompasses intelligent handling of security and data usage control across the federation of decentralized platforms instances to reduce data and service management complexity. The proposed solution is experimentally evaluated in terms of performances and validated against use cases. Further, this work adopts and extends available standards and open sources, after an analysis of their capabilities, fostering an easier acceptance of the proposed framework. We also report efforts to initiate an IoT services ecosystem among 27 cities in Europe and Korea based on a novel methodology. We believe that this thesis open a viable path towards a hyperconnection of IoT data and services, minimizing the human effort to manage it, but leaving the full control of the data and service management to the users' will

IIoT Data Ness: From Streaming to Added Value

In the emerging Industry 4.0 paradigm, the internet of things has been an innovation driver, allowing for environment visibility and control through sensor data analysis. However the data is of such volume and velocity that data quality cannot be assured by conventional architectures. It has been argued that the quality and observability of data are key to a project’s success, allowing users to interact with data more effectively and rapidly. In order for a project to become successful in this context, it is of imperative importance to incorporate data quality mechanisms in order to extract the most value out of data. If this goal is achieved one can expect enormous advantages that could lead to financial and innovation gains for the industry. To cope with this reality, this work presents a data mesh oriented methodology based on the state-of-the-art data management tools that exist to design a solution which leverages data quality in the Industrial Internet of Things (IIoT) space, through data contextualization. In order to achieve this goal, practices such as FAIR data principles and data observability concepts were incorporated into the solution. The result of this work allowed for the creation of an architecture that focuses on data and metadata management to elevate data context, ownership and quality.O conceito de Internet of Things (IoT) é um dos principais fatores de sucesso para a nova Indústria 4.0. Através de análise de dados sobre os valores que os sensores coletam no seu ambiente, é possível a construção uma plataforma capaz de identificar condições de sucesso e eventuais problemas antes que estes ocorram, resultando em ganho monetário relevante para as empresas. No entanto, este caso de uso não é de fácil implementação, devido à elevada quantidade e velocidade de dados proveniente de um ambiente de IIoT (Industrial Internet of Things)

On the use of information and infrastructure technologies for the smart city research in Europe: a survey

The Smart City paradigm has become one of the most important research topics around the globe. Particularly in Europe, it is considered as a solution for unstoppable increase of high density urban environments and the European Commission has included the Smart City research as one of the key objectives for the FP7 (Seventh Framework Program) and H2020 (Horizon 2020) research initiatives. As a result, a considerable amount of quality research, with particular emphasis on information and communication technologies, has been produced. In this paper, we review the current efforts dedicated in Europe to this research topic. Particular attention is paid in the review to the platforms and infrastructure technologies adopted to introduce the Internet of Things into the city, taking into account the constraints and harshness of urban environments. Furthermore, this paper also considers the efforts in the experimental perspective, which includes the review of existing Smart City testbeds, part of wider European initiatives such as FIRE (Future Internet Research and Experimentation) and FIWARE. Last but not least, the main efforts in providing interoperability between the different experimental facilities are also presented.This work was funded in part by the European Union’s Horizon 2020 Programme of the FESTIVAL project (Federated Interoperable Smart ICT Services Development and Testing Platforms) under grant agreement 643275, and from the Japanese National Institute of Information and Communications Technology

Advancing IoT Platforms Interoperability

The IoT European Platforms Initiative (IoT-EPI) projects are addressing the topic of Internet of Things and Platforms for Connected Smart Objects and aim to deliver an IoT extended into a web of platforms for connected devices and objects that supports smart environments, businesses, services and persons with dynamic and adaptive configuration capabilities. The specific areas of focus of the research activities are architectures and semantic interoperability, which reliably cover multiple use cases. The goal is to deliver dynamically-configured infrastructure and integration platforms for connected smart objects covering multiple technologies and multiple intelligent artefacts. The IoT-EPI ecosystem has been created with the objective of increasing the impact of the IoT-related European research and innovation, including seven European promising projects on IoT platforms: AGILE, BIG IoT, INTER-IoT, VICINITY, SymbIoTe, bIoTope, and TagItSmart.This white paper provides an insight regarding interoperability in the IoT platforms and ecosystems created and used by IoT-EPI. The scope of this document covers the interoperability aspects, challenges and approaches that cope with interoperability in the current existing IoT platforms and presents some insights regarding the future of interoperability in this context. It presents possible solutions, and a possible IoT interoperability platform architecture

The European Industrial Data Space (EIDS)

This research work has been performed in the framework of the Boost 4.0 Big Data lighthouse initiative, a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 780732. This datadriven digital transformation research is also endorsed by the Digital Factory Alliance (DFA)The path that the European Commission foresees to leverage data in the best possible way for the sake of European citizens and the digital single market clearly addresses the need for a European Data Space. This data space must follow the rules, derived from European values. The European Data Strategy rests on four pillars: (1) Governance framework for access and use; (2) Investments in Europe’s data capabilities and infrastructures; (3) Competences and skills of individuals and SMEs; (4) Common European Data Spaces in nine strategic areas such as industrial manufacturing, mobility, health, and energy. The project BOOST 4.0 developed a prototype for the industrial manufacturing sector, called European Industrial Data Space (EIDS), an endeavour of 53 companies. The publication will show the developed architectural pattern as well as the developed components and introduce the required infrastructure that was developed for the EIDS. Additionally, the population of such a data space with Big Data enabled services and platforms is described and will be enriched with the perspective of the pilots that have been build based on EIDS.publishersversionpublishe

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

IoT data processing pipeline in FoF perspective

With the development in the contemporary industry, the concepts of ICT and IoT are gaining more importance, as they are the foundation for the systems of the future. Most of the current solutions converge into transforming the traditional industry in new smart interconnected factories, aware of its context, adaptable to different environments and capable of fully using its resources. However, the full potential for ICT manufacturing has not been achieved, since there is not a universal or standard architecture or model that can be applied to all the existing systems, to tackle the heterogeneity of the existing devices. In a common factory, exists a large amount of information that needs to be processed into the system in order to define event rules accordingly to the related contextual knowledge, to later execute the needed actions. However, this information is sometimes heterogeneous, meaning that it cannot be accessed or understood by the components of the system. This dissertation analyses the existing theories and models that may lead to seamless and homogeneous data exchange and contextual interpretation. A framework based on these theories is proposed in this dissertation, that aims to explore the situational context formalization in order to adequately provide appropriate actions

MONICA in Hamburg: Towards Large-Scale IoT Deployments in a Smart City

Modern cities and metropolitan areas all over the world face new management challenges in the 21st century primarily due to increasing demands on living standards by the urban population. These challenges range from climate change, pollution, transportation, and citizen engagement, to urban planning, and security threats. The primary goal of a Smart City is to counteract these problems and mitigate their effects by means of modern ICT to improve urban administration and infrastructure. Key ideas are to utilise network communication to inter-connect public authorities; but also to deploy and integrate numerous sensors and actuators throughout the city infrastructure - which is also widely known as the Internet of Things (IoT). Thus, IoT technologies will be an integral part and key enabler to achieve many objectives of the Smart City vision. The contributions of this paper are as follows. We first examine a number of IoT platforms, technologies and network standards that can help to foster a Smart City environment. Second, we introduce the EU project MONICA which aims for demonstration of large-scale IoT deployments at public, inner-city events and give an overview on its IoT platform architecture. And third, we provide a case-study report on SmartCity activities by the City of Hamburg and provide insights on recent (on-going) field tests of a vertically integrated, end-to-end IoT sensor application.Comment: 6 page

Open Interfaces for Connecting Automated Guided Vehicles to a Fleet Management System

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