Enabling IoT ecosystems through platform interoperability

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.Peer ReviewedPostprint (author's final draft

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Maintenance Knowledge Management with Fusion of CMMS and CM

Abstract- Maintenance can be considered as an information, knowledge processing and management system. The management of knowledge resources in maintenance is a relatively new issue compared to Computerized Maintenance Management Systems (CMMS) and Condition Monitoring (CM) approaches and systems. Information Communication technologies (ICT) systems including CMMS, CM and enterprise administrative systems amongst others are effective in supplying data and in some cases information. In order to be effective the availability of high-quality knowledge, skills and expertise are needed for effective analysis and decision-making based on the supplied information and data. Information and data are not by themselves enough, knowledge, experience and skills are the key factors when maximizing the usability of the collected data and information. Thus, effective knowledge management (KM) is growing in importance, especially in advanced processes and management of advanced and expensive assets. Therefore efforts to successfully integrate maintenance knowledge management processes with accurate information from CMMSs and CM systems will be vital due to the increasing complexities of the overall systems. Low maintenance effectiveness costs money and resources since normal and stable production cannot be upheld and maintained over time, lowered maintenance effectiveness can have a substantial impact on the organizations ability to obtain stable flows of income and control costs in the overall process. Ineffective maintenance is often dependent on faulty decisions, mistakes due to lack of experience and lack of functional systems for effective information exchange [10]. Thus, access to knowledge, experience and skills resources in combination with functional collaboration structures can be regarded as vital components for a high maintenance effectiveness solution. Maintenance effectiveness depends in part on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. Maintenance effectiveness, to a large extent, also depends on the quality of the knowledge of the managers and maintenance operators and the effectiveness of the internal & external collaborative environments. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of ICT) in organizations, the conceptualization and implementation of E-Maintenance is turning into a reality. Unfortunately, even though knowledge management aspects are important in maintenance, the integration of KM aspects has still to find its place in E-Maintenance and in the overall information flows of larger-scale maintenance solutions. Nowadays, two main systems are implemented in most maintenance departments: Firstly, Computer Maintenance Management Systems (CMMS), the core of traditional maintenance record-keeping practices that often facilitate the usage of textual descriptions of faults and actions performed on an asset. Secondly, condition monitoring systems (CMS). Recently developed (CMS) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been limited in their approach and scalability. In this article we present one approach for addressing this challenge. We argue that understanding the requirements and constraints in conjunction - from maintenance, knowledge management and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful integration of such disparate data types and processes (also integrating knowledge management into the “data types” and processes)

The European Institute for Innovation through Health Data

The European Institute for Innovation through Health Data (i~HD, www.i-hd.eu) has been formed as one of the key sustainable entities arising from the Electronic Health Records for Clinical Research (IMI-JU-115189) and SemanticHealthNet (FP7-288408) projects, in collaboration with several other European projects and initiatives supported by the European Commission. i~HD is a European not-for-profit body, registered in Belgium through Royal Assent. i~HD has been established to tackle areas of challenge in the successful scaling up of innovations that critically rely on high-quality and interoperable health data. It will specifically address obstacles and opportunities to using health data by collating, developing, and promoting best practices in information governance and in semantic interoperability. It will help to sustain and propagate the results of health information and communication technology (ICT) research that enables better use of health data, assessing and optimizing their novel value wherever possible. i~HD has been formed after wide consultation and engagement of many stakeholders to develop methods, solutions, and services that can help to maximize the value obtained by all stakeholders from health data. It will support innovations in health maintenance, health care delivery, and knowledge discovery while ensuring compliance with all legal prerequisites, especially regarding the insurance of patient's privacy protection. It is bringing multiple stakeholder groups together so as to ensure that future solutions serve their collective needs and can be readily adopted affordably and at scale

ERIGrid Holistic Test Description for Validating Cyber-Physical Energy Systems

Smart energy solutions aim to modify and optimise the operation of existing energy infrastructure. Such cyber-physical technology must be mature before deployment to the actual infrastructure, and competitive solutions will have to be compliant to standards still under development. Achieving this technology readiness and harmonisation requires reproducible experiments and appropriately realistic testing environments. Such testbeds for multi-domain cyber-physical experiments are complex in and of themselves. This work addresses a method for the scoping and design of experiments where both testbed and solution each require detailed expertise. This empirical work first revisited present test description approaches, developed a newdescription method for cyber-physical energy systems testing, and matured it by means of user involvement. The new Holistic Test Description (HTD) method facilitates the conception, deconstruction and reproduction of complex experimental designs in the domains of cyber-physical energy systems. This work develops the background and motivation, offers a guideline and examples to the proposed approach, and summarises experience from three years of its application.This work received funding in the European Community’s Horizon 2020 Program (H2020/2014–2020) under project “ERIGrid” (Grant Agreement No. 654113)

Developing front-end Web 2.0 technologies to access services, content and things in the future Internet

The future Internet is expected to be composed of a mesh of interoperable web services accessible from all over the web. This approach has not yet caught on since global user?service interaction is still an open issue. This paper states one vision with regard to next-generation front-end Web 2.0 technology that will enable integrated access to services, contents and things in the future Internet. In this paper, we illustrate how front-ends that wrap traditional services and resources can be tailored to the needs of end users, converting end users into prosumers (creators and consumers of service-based applications). To do this, we propose an architecture that end users without programming skills can use to create front-ends, consult catalogues of resources tailored to their needs, easily integrate and coordinate front-ends and create composite applications to orchestrate services in their back-end. The paper includes a case study illustrating that current user-centred web development tools are at a very early stage of evolution. We provide statistical data on how the proposed architecture improves these tools. This paper is based on research conducted by the Service Front End (SFE) Open Alliance initiative

European (energy) data exchange reference architecture 3.0

This is the third version of Data Exchange Reference Architecture – DERA 3.0. BRIDGE report on energy data exchange reference architecture aims at contributing to the discussion and practical steps towards truly interoperable and business process agnostic data exchange arrangements on European scale both inside energy domain and across different domains.DERA 3.0Recommendations related to the implementation of DERA:A. Leverage Smart Grid Architecture Model (SGAM) usage by completing it with data governance requirements, specifically from end-customer perspective, and map it to the reference architectures of other sectors (similar to the RAMI4.0 for industry – Reference Architecture Model Industrie 4.0; and CREATE-IoT 3D RAM for health – Reference Architecture Model of CREATE-IoT project), incl. for basic interoperability vocabulary with non-energy sectors.B. Facilitate European strategy, regulation (harmonisation of national regulations) and practical tools for cross-sector exchange of any type of both private data and public data, e.g. through reference models for data space, common data governance and data interoperability implementing acts.C. Ensure cooperation between appropriate associations, countries and sector representatives to work on cross-sector and cross-border data management by establishing European data cooperation agency. This involves ongoing empowering/restructuring of the Data Management WG of the BRIDGE Initiative to engage other sectors and extend cooperation with projects that are not EU-funded and with European Standardisation Organisations (CEN-CENELEC-ETSI).D. Harmonise the development, content and accessibility of data exchange business use cases for cross-sector domain through BRIDGE use case repository. Track tools that identify common features on use cases, e.g. interfaces between sectors, and enable the alignment with any potential peer repositories for other domains. Also, the use case repository must rely on the HEMRM with additional roles created by some projects or roles coming from other associations (related to another sector than the electricity/energy sector).E. Use BRIDGE use case repository for aligning the role selection. Harmonise data roles across electricity and other energy domains by developing HERM – Harmonised Energy Role Model and ensure access to model files. Look for consistency with other domains outside energy based on this HERM – cross-sectoral roles. Harmonised EnergyData EndpointsData SpaceConnectorData ProcessingStandard CommunicationProtocols& FormatsData HarmonizationData PersistanceVocabularyProviderCredentialManagerIdentityManagerMonitoring& OrchestrationData DiscoveryData IndexerLocal AI/ML ServicesDigital TwinsMarketplace BackendStandard CommunicationProtocols& FormatsMarketplace FrontendFederatedUse Cases and Business needsLocal Use Cases and Business needsEnergy RegulationEU Re-gulationActorsBusinessFunctionInformationComp.CommsNon-personal dataSecurity/ResilienceUserAcceptanceSovereigntyOpen SourceInteroperabilityLocalFederatedInteroperabilityTrustData valueGovernance9DATA MANAGEMENT WORKING GROUPEuropean (energy) data exchange reference architecture 3.0Role Model shall have clear implications and connections with data (space) roles such as data provider/consumer, service provider etc.F. Define and harmonise functional data processes for cross-sector domain, using common vocabulary, template and repository for respective use cases’ descriptions. Harmonisation of functional data processes for cross-sector data ecosystems including Vocabulary provider, Federated catalogue, Data quality, Data accounting processes, Clearing process (audit, logging, etc.) and Data tracking and provenance.G. Define and maintain a common reference semantic data model, and ensure access to its model files facilitating cross-sector data exchange, by leveraging existing data models like Common Information Model (CIM) of International Electrotechnical Commission (IEC) and ontologies like Smart Appliances Reference Ontology (SAREF).H. Develop cross-sector data models and profiles, with specific focus on private data exchange. Enable open access to model files whenever possible.I. Ensure protocol agnostic approach to cross-sector data exchange by selecting standardised and open ones.J. Ensure data format agnostic approach to cross-sector data exchange. The work done by projects like TDX-ASSIST and EU-SysFlex (using IEC CIM), and PLATOON (using SAREF) must be shared and made known to consolidate the approach in order to reach semantic interoperability. Metadata must also be taken into account.K. Promote business process agnostic DEPs (Data Exchange Platforms) and make these interoperable by developing APIs (Application Programming Interfaces) which enable for data providers and data users easy connection to any European DEP but also create the possibility whereby connecting to one DEP ensures data exchange with any other stakeholder in Europe. DEPs shall explore the integration of data space connectors towards their connectivity with other DEPs including cross-sector ones.L. Develop universal data applications which can serve any domain. Develop open data driven services that promote also cross-sector integration collectively available in application repositories.Possible next steps (“sub-actions”) for 2023/2024:➢ Release BRIDGE Federated Service Catalogue tool and associated process.➢ Release DERA interactive visualisation tool.➢ Follow up the implementation of DERA 3.0 in BRIDGE projects (mapping to DERA)➢ Update recommendations to comply with DERA 3.0.➢ Develop / enhance the “data role model”