This document is a quick start guide to the BRAIN-IoT Modelling & Validation framework implementation, i.e., a design tool hereafter named BRAIN-IoT Services Development Toolkit”. BRAIN-IoT Services Development Toolkit is an Eclipse Rich Client Platform (RCP).
The tool is composed of the Papyrus IoT-ML modeller, W3C WoT TD modelle, BIP modeller, AIML modeller, and sensiNact data model modeller. These modelling languages and their modellers are integrated within a same UML and Eclipse framework. More information on the underlying modelling languages can be found in D3.5. In addition to these modellers, BRAIN-IoT Services Development Toolkit offers Model-Driven Engineering (MDE) tools for purposes such as model checking, model verification, code and interface description generation, and monitoring.
This document shows how to download, install, launch, and create a first IoT-ML model with BRAIN-IoT Services Development Toolkit. We show how the model can be annotated with concepts of W3C WoT TD and sensiNact. Then the model can be refined as system software components which are represented in the BIP modelling and analysis framework for code generation. Finally, this document shows the work have been done for the final version of the tool. These MDE tools in development exploit the model for metadata generation and add Models@runtime functionalities, i.e., monitoring and quick post-deployment behaviour prototyping through models.
The deliverable D2.3, titled “First Landscape and ESG workshop report” is the first outcome of T2.2 “Alignment with on-going IoT-related initiatives, roadmaps and the ESG”.
As per [RD.1], “This task will continuously monitor state-of-the-art research, policy and commercial initiatives delivering tangible results, standards, regulations or lessons learned which are relevant for BRAIN-IoT developments and further business exploitation. Input from monitored initiatives will be used to perform analysis to identify and exploit synergies with other on-going initiatives and forecast of technical trends. In order to ensure relevance and correctness of analysed synergies, this task will leverage the collaboration of representative external experts, namely the External Stakeholder Group (ESG), which will support the project with the identification of potential values of use cases for stakeholders and uncertainties related to business and technology trends, as well as providing their view on the BRAIN-IoT potential for development. As further detailed in Section 12, interaction with the ESG will be managed jointly by LINKS, UGA, and IDATE, led by partner CEA. ISMB has set aside a dedicated budget to organize and support ESG activities. CEA will lead the task by ensuring that identified synergies are properly reflected in BRAIN-IoT specifications.
LINKS and UGA will ensure alignment with initiatives and roadmaps in the areas of IoT and CPS. PAREMUS will focus on inputs from key standardization initiatives in the area of middleware. AIRBUS will support the activity by focusing on security and privacy aspects, providing inputs from cyber-security IoT initiatives, especially with the French ANSSI agency and similar initiatives. IDATE will support the activity ensuring alignment with technology trends identified through IDATE reports, desk research and other projects with similar activities like CREATE-IoT. Results will be documented in deliverables D2.3 and D2.8.”
The present document is a deliverable of the BRAIN-IoT project, funded by the European Commission, under its Horizon 2020 Research and innovation program (H2020), reporting the validation results of the activities carried out by “WP6 – Test, Demonstration and Evaluation”. This deliverable includes the results of the validation methodology described in the “Deliverable D6.2 – Integration and Lab Scale Evaluation” and “Deliverable D6.3 – Phase 1 Integration and Evaluation Framework” and it follows the presentation philosophy of validation results in some production environments. The deliverable does not extend deeply the sections included in the previous D6.2 and D6.3 more than necessary, but it adds information and remarks on important aspects related to the validation methodology needed for the good comprehension of the process and that they were not indicated in the D6.3 because the approach was not yet fully defined at the time of writing that deliverable.
The development activities are being performed in the WP3, WP4, and WP5, and the activities of verification, validation, and evaluation will be reported in the deliverables 6.3, 6.4, 6.5, and 6.6 of WP6.
This deliverable includes 5 sections. Section 1 introduces a summary of concepts related to the validation procedures. Section 2 presents the process of validation management including the methodology. Section 3 is the main section, and includes the results of the validation activities during the first validation iteration, indicating the status of every test and requirement defined in the previous activities. Section 4 contains the status of the tests and demonstrations regarding the KPIs defined in the Grant Agreement and extended in the deliverable D6.2. Section 5 provides the main conclusions related to the validation process and results.
This document reports the activities performed in Task 5.2. This task aims at designing and implementing an Authentication, Authorization and Accounting (AAA) layer in order to provide security and trust for the BRAIN-IoT system. This layer ensures Authentication and Access Management to allow only enrolled and authorized devices or users to access a service. It aims at overcoming the current state-of-the-art by:
- Providing only one solution to manage Users and Devices authentication and access control
- Ensuring strong authentication for low power devices
- Optimizing key management
Actually, a large number of devices and their specificities in IoT systems lead indeed to complex management activities with high operating costs, especially regarding security management: identity, cryptographic keys, and right management.
The present document is the deliverable D5.5 of the BRAIN-IoT project, funded by the European Commission, under its Horizon 2020 Research and innovation program (H2020), reporting the results of the activities carried out by WP5 – End-to-end Security, Privacy and Trust Enablers. The work collected in this document has been compiled with a collaborative effort of all partners who actively participated in Task 5.1 – Threat Modeling and Assessment of BRAIN-IoT target scenarios: Critical Water Management Infrastructure and Service Robotics.
The first report on threat modeling and security assessment has been provided in deliverable D5.1 at M6. In deliverable D5.1 “Initial Threat Modeling and Security Assessment of Target Scenarios, Solutions”, we have focused on the definition of the risk assessment methodology and provided an initial risk assessment of the target scenarios as an illustration of the first phases of the methodology, namely, the identification of critical assets and associated threats and vulnerabilities.
The results of the second iteration have been provided in deliverable D5.4 at M16. In deliverable D5.4 “Updated Threat Modeling and Security Assessment of Target Scenarios, Solutions”, we have continued the risk assessment process through one more iteration by updating and refining the set of assets and their associated threats, for each scenario. We have also made progress in the risk assessment methodology by identifying security objectives that cover the full list of threats for each asset and elaborating technical security requirements covering various security objectives.
This deliverable D5.5 gives the final report on threats modeling and the security assessment process. The novel technical contributions with respect to the previous deliverable D5.4 are:
- In sections 5 and 6, we update the list of assets and their associated threats, security objectives, and security requirements for both Critical Water Management Infrastructure and Service Robotics scenarios.
- In section 7, we present a technological solution to implement the security requirements and explain the innovation of BRAIN-IoT solutions compared to the state of the art.
- In section 8, we collect quantitative metrics of attacks that can exploit several threats and vulnerabilities. Then, we explore defense configurations with the highest impact on attacks.
The IoT domain comprises several different governance models, which are often incompatible, this leads to a situation where security is treated on a per-case and per-legislation basis, retrofitting solutions to existing designs, and this severely hampers portability, interoperability, and deployment. This motivates, first, adopting a Reference Model for the BRAIN-IoT domain in order to promote a common understanding and a common ground for IoT solutions. Second, solutions should describe the essential building blocks regarding functionality, development, deployment, and security schemes.
This document presents the updated reference architecture stemming from deliverable 2.5 and deliverable 2.2 versus requirements that rely on technologies supported by our BRAIN-IoT partners.
The main objective of work package 7 is to create awareness and adoption of the BRAIN-IoT project within the targeted communities defined by deliverables D7.3 & D7.1, i.e.: research communities, developer communities (Early adopters and Late adopters), solution makers, end-users and the general public.
This report documents the updated efforts and results of the project in terms of advertising and community engagement. It follows the dissemination strategy defined in D7.1 and the first version of the advertising, community engagement materials, and results available in D7.3.
In this document, a list of the press releases, articles, social media tools, and available contents will be presented, as well as a brief summary of the events in which the project has participated.
This document will present the different ideas composing the frameworks that the BRAIN-IoT consortium is considering for the integration and evaluation of separated results of all technical work packages.
The technical evaluation Framework initially defined by Task 6.1 will be presented in the first sections by introducing our lab-scale Proof of Concepts.
Then, our main concepts for user-centered evaluation will be introduced by highlighting the expectations of the end-user partners, and the way the BRAIN-IoT developments will support the operative work. The end-user related KPI’s will also be revised in this section.
Finally, the test-site evaluation framework will be presented by describing the real demonstrators that are foreseen to be part of the project at this point.
The present document is a deliverable of the BRAIN-IoT project, funded by the European Commission, under its Horizon 2020 Research and innovation program (H2020), reporting the results of the activities carried out by WP6 – Test, Demonstration and Evaluation. The main objective of the BRAIN-IoT project is to focus on complex scenarios, where populations of heterogeneous IoT systems cooperatively support actuation and control. In such a complex context, many initiatives fall into the temptation of developing new IoT platforms, protocols, models or tools aiming to deliver the ultimate solution that will solve all the IoT challenges and become ”The” reference IoT platform or standard. Instead, usually they result in the creation of “yet-another” IoT solution or standard. More specifically, the project revolves around two vision scenarios; Service Robotics and Critical Infrastructure Management. The scenarios outlined in the proposal are refined within the engineering efforts alongside the project, driven by WP2.
This deliverable defines the different steps, methods and tools required to verify and validate the architecture of the BRAIN-IoT project and its two related use cases. It also provides the timeline to indicate the achievements of these validations. Furthermore, it provides complementary KPIs associated with the expected outcomes from the BRAIN-IoT architecture concerning its main building blocks, the two site use cases and the tools.
The development activities are being performed in the WP3, WP4 and WP5 and the activities of verification, validation and evaluation will be reported in the deliverables 6.3, 6.4, 6.5 and 6.6 of WP6.
This deliverable includes 6 sections. Section 1 introduces the activities of WP6 and more precisely the activities performed in Task 6.1. It briefly describes the expectations of this deliverable. Section 2 presents the different steps to follow the development, verification and validation of the BRAIN-IoT System architecture and its corresponding use cases. Each step is associated with a Proof-of-Concept (PoC) that will be developed in order to demonstrate the main concepts proposed by the BRAIN-IoT project. At the end of Section 2, a PoCs implementation schedule is provided. Section 3 gives more details on the two use cases (Service Robotics and Critical Infrastructure Management) that will be implemented and demonstrated to validate the architecture and concepts developed in BRAIN-IoT. Section 4 proposes a verification and validation methodology, a test plan and a modelling framework, and its associated tools, to perform the BRAIN-IoT system validation. At the end of Section 4, a tool implementation schedule is provided. Section 5 provides a list of KPIs related to the expected outcomes coming from the different main elements constituting the BRAIN-IoT architecture, the Modelling Framework tools, and the two use cases. Section 6 concludes this work.
The present document is a deliverable of the BRAIN-IoT project, funded by the European Commission, under its Horizon 2020 Research and innovation program (H2020), reporting the results of the activities carried out by WP5 – End-to-end Security, Privacy and Trust Enablers. The work collected in this document has been compiled with a collaborative effort of all partners who actively participated in the Task 5.1 – Threat Modeling and Assessment.
The first report on threat modeling and security assessment has been provided in deliverable D5.1 at M6. This document has reported on the initial threat modeling and preliminary security assessment of the BRAIN-IoT proposed scenarios and introduces the principles of the BRAIN-IoT security methodology based on known threats analysed by international initiatives undergoing in the EU and worldwide. Starting from the scenarios and architectural solutions defined by WP2, the involved partners performed an initial analysis considering intentional threats that may result in BRAIN-IoT services to be compromised or disrupted.
This deliverable D5.4 is an updated and extended version of D5.1, where the partners refined the threats identified previously for the two use cases (“Critical Water Management Infrastructure” and “Service Robotics”) and completed several additional phases of the proposed security methodology. In particular, the risk assessment process has been extended to include the security objectives and the associated security requirements. The novel technical contributions with respect to the previous deliverable D5.2 are described in the following sections:
- Sections 4.4 Security Objectives and 4.5 Security Requirements provide generic objectives and requirements relevant for the Brain-IoT methodology.
- Sections 5.3 Security Objectives and 5.4 Security Requirements for the Critical Water Management Infrastructure.
- Sections 6.3 Security Objectives and 6.4 Security Requirements for the Service Robotics.
Finally, the work presented in this deliverable will be continued in the next iteration and updated in the deliverable D5.5, where the final results will be documented.
This document is a deliverable of the BRAIN-IoT project, funded by the European Commission, under Horizon 2020 Research and Innovation Program (H2020). It belongs to WP5 – End-to-end Security, Privacy and Trust Enablers, under Task 5.3- Initial enabler for privacy awareness and control.
Scope and goals
The main scope of this task is to facilitate the adoption of privacy control policies in decentralized environments building upon goals set by current EU General Data Protection Regulation (GDPR) regulations in terms of privacy and security. More specifically, the goal of this task is to integrate privacy awareness and control in programmatic ways to: (i) increase awareness of users about which data is collected, where it is transmitted, by whom, etc.; (ii) provide controls to enable users to control such aspects, being at the same time aware of how such a decision affect the quality of the IoT service provided in BRAIN-IoT. In the context of privacy awareness and control, the primary objectives are the identification of the privacy risks and the associated technical requirements towards proposing privacy risk mitigation strategies to protect the individuals data (both private and corporate users) the BRAIN-IoT system. The activities performed by Task 5.3 are the following:
- Study of the state-of-the-art in the context of privacy risk assessment and control;
- Propose an approach for privacy impact assessment based on the guidelines from GDPR;
- Initial privacy impact assessment of the BRAIN-IoT use cases;
- Outline the components needed to integrate privacy policies within the Brain-IoT system.
The proposed solution will be cross-platform, so to possibly support a wide number of IoT products deployed by corporate and private users, therefore empowering final users (both private and corporate) with the capability of deciding which combination of self-hosted or cloud-oriented IoT systems is most suitable to handle the personal data they generate and own – as well as with the ability to change the existing (or pre-set) configurations at any time. Deliverable D5.3 will be continuously updated and refined through an iterative process that will lead to the production of two additional deliverables; D5.7 Final enablers for Privacy awareness and control, solutions planned on M32 and D5.9 Guidelines for privacy compliance and control in IoT services models, solutions planned on M36. LINKS is in charge to coordinate these deliverables with contributions from CNRS, IM, STM-GNB, AIRBUS, EMALCSA, and Robotnik.
This document reports the activities performed in Task 5.2. This task aims at designing and implementing an Authentication, Authorization and Accounting (AAA) layer in order to provide security and trust for the Brain-IoT system.
A BRAIN-IoT environment is composed of a number of BRAIN-IoT Fabrics. These are in-turn composed of a number of BRAIN-IoT nodes (see the related document RD.1 in §1.2). A BRAIN-IoT environment may consist of arbitrary complex distributed interactions between dynamically deployed Smart Behaviours. These behaviours may migrate between runtime locations within each Fabric environment (see the related document RD.2 in §1.2).
To be discovered, and to participate as a member of a Fabric, each BRAIN-IoT node must have the appropriate X509 certificates. Certificates and TLS are both good defenses against external Man-in-the-middle (MITM) attacks but are insufficient against internal MITM. So to guard against a Smart Behaviour erroneously (or maliciously) editing data for an event and sending them on, the project requires that each BRAIN-IoT message contains its own Authentication token.
Finally, BRAIN-IoT must ensure that Smart Behaviours only interact at runtime in an expected manner.
Security is an extremely broad subject within computer science, and it can quickly become difficult to describe the complete set of security actions that apply to a software system. In this deliverable the scope of security is limited to the following considerations:
- The registration of identity for users, devices and Smart Behaviours within the Brain-IoT system.
- The authentication of users, devices and Smart Behaviours.
- The validation of data integrity for messages passed through the Brain-IoT system.
- Permission management for users, devices and Smart Behaviours, limiting the permitted actions and data interactions.
- The runtime application of permissions to permit or deny access.
This report presents the initial perspective of a potential business strategy around the BRAIN IoT repository / marketplace.
To assess this potential perspective the consortium has worked in three different, complementary pathways:
- A careful definition of the repository functional and technical requirements (section 2)
- An assessment of existing other examples of repository/marketplace from known, comparable European projects (COMPOSITION and Big IoT, presented in section 3).
- A review of the business approach of existing software marketplaces developed by various types of businesses and industries (telecom operators, cloud platforms, software repositories, application enablement platforms…). (section 4)
Through this cross-analysis, a first definition of the potential business requirements of a BRAIN IoT marketplace has been defined (section 2.2).
The main findings can be summed up as follow:
- The development of the marketplace would provide benefits to various stakeholders by bringing additional value to the technology platform, but the sustainability of the approach still needs to be assessed, especially if engagement of developers remains too limited.
- The value of a `Marketplace` is dictated by the shareability/re-usability of artefacts in the Marketplace in different and unrelated runtime contexts.
- Generic high-quality Edge Integration Components with assured pedigree/provenance will be of interest to a large community. Whereas, Sophisticated AI/ML-based behaviours trained for specific roles in specific environments may have extremely high business value in the target environment, but limited applicability elsewhere. Here, it is the generic modelling tools which allowed the simple creation of context specific AI/ML-based behaviours that will be of interest to the wider community.
- A Brain IoT marketplace would have to first attract a critical mass of contributors that can build potential behaviours. Thus, a focus on the modelling tool community, smart behaviour developers, should be considered. Ideally the marketplace should also initially be populated with simple behaviours that can be composed easily into more complex ones to facilitate adoption. In a second time the marketplace would have to focus on IoT users, deployment operators and integrators that would need the behaviour developed by the modelling tool community.
- To compensate the marketplace costs and build up the marketplace community, a business model using a subscription model is considered as a more realistic short-term option than a traditional model that takes a share of marketplace exchanges. This is especially the case if the marketplace focuses at first on small atomic behaviours to be assembled. Building up on these initial findings, further discussions are planned as part of the project work on exploitation in the M18-M36 activity period to identify if such a business dynamics can be integrated in the exploitation plan of some of the project partners
Building a framework for deployment and operation of Internet of Things (IoT) service orchestration is a complex task since it needs to address two major challenges: a strong availability and an abstraction layer to deal with heterogeneous devices. In order to tackle the challenge of availability, the Brain IoT project has chosen to rely on Paremus service Fabric, which provides distribution, monitoring, and automatic recovery in case of node failure. Regarding the interaction with heterogeneous devices, the Eclipse sensiNact middleware2 has been chosen for its capability to interact with a wide variety of equipment and protocols, as well as its extensibility mechanisms. The purpose of deliverable 4.2 is to provide a platform using the best of those two enablers, thanks to evolutions of the two code-bases in order to integrate them gracefully. For this reason, deliverable 4.2 is provided as source code.
This deliverable is a software release type of deliverable. The goal of the present notice is to help technical and non-technical stakeholders to understand the purpose of the source code which has been delivered, and to provide useful links to retrieve, build and run the platform.
The software stack is still under construction. It will be delivered in its final version at the end of the project, as part of deliverable 4.5 entitled “Final Deployment and operation enablers”
This document is a quick start guide to the BRAIN-IoT modelling framework implementation, i.e., a modelling tool hereafter named “BRAIN-IoT Designer”. The modelling tool is composed of the Papyrus IoT-ML modeller, and its eco-system od Model-Driven Engineering (MDE) tools for purposes such as model checking, code and metadata generation, and Models@runtime features. In this first version of BRAIN-IoT Designer, we shall expose its IoT-ML modeller and code generation functionalities through BIP. More information on the underlying modelling languages can be found in D3.1.
BRAIN-IoT Designer is an Eclipse Rich Client Platform (RCP) that has been built with all the required features packaged as one tool. In this first deliverable, BRAIN-IoT Designer is composed of the following features:
- Papyrus UML
- Papyrus SysML
- Papyrus MARTE
- Papyrus IoT-ML
This document shows how to download, install, launch, and create a first model with BRAIN-IoT Designer. Afterward we show how the model is represented in the BIP modelling and analysis framework for code generation. Finally, this document shows some ongoing development works for the second release of the tool, to be detailed in D3.8. These MDE tools in development exploit the model for metadata generation and add Models@runtime features, i.e., monitoring and quick post-deployment behavior prototyping through models.
This document describes the research and approaches identified by task 3.3 `Initial Enablers for dynamic distribution of IoT behaviour`: this activity delivering BRAIN-IoT’s generic nonfunctional runtime mechanisms needed to support the dynamic deployment and orchestration of BRAIN-IoT’s Smart Behaviours.
Why this is an essential characteristic of, and a unique differentiator for, BRAIN-IoT, is first explained.
It is important to note that this document is not concerned with the specifics of Smart Behaviours needed for particular Use Cases specified in D2.4; nor how Smart Behaviours should be modelled; these concerns addressed by the D3.1, D3.2 & D3.4 deliverables in WP3.
This document is a deliverable of the BRAIN-IoT project, funded by the European Commission, under the Horizon 2020 Research and Innovation Program (H2020). It belongs to WP3 – IoT Framework for smart dynamic behavior, under Task 3.2 – AI and ML features for smart behavior and actuation.
The main role of this task is to design and implement the features that deal with Artificial Intelligence (AI) and Machine Learning (ML) techniques in Smart Behaviours as they are being defined in BRAIN-IoT.
The two main sets of use-case real-world scenarios, service robotics, and critical infrastructure monitoring, will dictate the specific features where some applied intelligence (analysis, prediction, collaborative context base behavior) is needed to solve the problems.
However, it is the main goal of this document to generalize the solutions available for this kind of intelligence. Research in classic and state-of-the-art AI and ML methods will be elaborated on in order to design for a set of abstractions that can be used under a generalized approach. In such a way that this proposed design:
- Solves the use-case scenarios.
- Covers a wide enough range of problems in the context of Smart Behaviours in distributed environments.
- Is well defined in terms of the ongoing IoT-ML definition elements.
- Explore the Capabilities that would be needed to advertise regarding Smart Behaviours requirements.
- Outline which Smart Behaviours would benefit from the exogenous coordination approach advocated and developed in BIP.
- Those elements can be modelled and managed within the Brain-IoT developed modelling tools.
The IoT domain comprises several different governance models, which are often incompatible, this leads to a situation where security is treated on a per-case and per-legislation basis, retrofitting solutions to existing designs, and this severely hampers portability, interoperability, and deployment. In our vision of the Internet of Things, the interoperability of solutions at the communication level, as well as at the service level, has to be ensured across various platforms. This motivates, first, adopting a Reference Model for the BRAIN-IoT domain in order to promote a common understanding and a common ground for IoT solutions. Second, solutions should be supported by BRAIN-IoT Reference Architecture that describes essential building blocks regarding functionality, deployment, and security. The reference architecture proposed in this document relies on the technology supported by our BRAIN-IoT partners such as PAREMUS, CEA, and, Airbus.
The present document is a deliverable of the BRAIN-IoT project, funded by the European Commission, under its Horizon 2020 Research and innovation program (H2020), reporting the results of the activities carried out by WP2 – Requirements and Architecture Engineering. The main objective of the BRAIN-IoT project is to focus on complex scenarios, where populations of heterogeneous IoT systems cooperatively support actuation and control. In such a complex context, many initiatives fall into the temptation of developing new IoT platforms, protocols, models, or tools aiming to deliver the ultimate solution that will solve all the IoT challenges and become ”the” reference IoT platform or standard. Instead, usually, they result in the creation of a “yet-another” IoT solution or standard. More specifically, the project revolves around two vision scenarios; Service Robotics and Critical Infrastructure Management.
Deliverable D2.1 has reported on 1) the identification of the purpose and workflow of the workbench, 2) an initial set of stakeholders and their categorization, 3) the communication flow between them, and lastly 4) an initial description and analysis of use cases.
In the current deliverable D2.4, we refine the initial version of the use cases specified in D2.1 and give an updated vision towards which the project will evolve. The descriptions presented here will be used for interproject communication in order to identify development scenarios and help to fertilize the process of thinking for the design of future systems. Besides the aspects regarding development, the vision scenarios are used as understandable stories to externally communicate the project’s aims and inform the audience what kind of applications can be designed with the framework developed by BRAIN-IoT, the actual outcome of the project.
Finally, deliverable D2.4 will be further extended and refined in the next iteration and updated in the deliverable D2.6, where the final results will be documented.
The main objective of work package 7 is to create awareness and adoption of the BRAIN-IoT project within the targeted communities defined by deliverables D7.4 & D7.1, i.e.: Research Communities, Developer Communities (Early adopters and Late adopters), Solution makers, End users and the general public
This report documents the initial efforts and results of the project in term of advertising and community engagement. It follows the dissemination strategy defined in D7.1.
In this document, a list of the press releases, articles, social media tools and available contents will be presented, as well as a brief summary of the events in which the project has participated.
This deliverable documents the activities related to the website of the Brain-IoT project, the main tool to communicate project results to scientists, scholars, professionals, the interested public and other stakeholders. More specifically, it aims at providing a project introduction as well as continuous updates on project results. The website is designed using the WordPress Content Management System (CMS), as it naturally supports the combination of static pages with blog entries that are continuously added. WordPress also provides an editorial system that supports the coordination of inputs from the different partners and collaborators of the project.
This document defines the project‘s outreach strategy including an effective communication plan. The strategy is intended to optimize dissemination of project knowledge and results to scientific, open source and industrial communities, companies and public organizations. This document will identify the main stakeholders‘ communities to be mobilized by the project and for each define the best media, events, and publications to target.
This document will be a living document throughout the project regularly updated to take count of strategy evolutions. The evolution of the strategy will be visible in the next deliverables D7.3, D7.5 and D7.6 which will present an update of the Advertising, Community Engagement materials and Results.
This document is split in three main sections:
- How we identify our customers, describing the projects’ efforts to identify the dissemination and exploitations targets, i.e. the project “customers”.
- The Dissemination strategy outlining the dissemination activities carried out by the BRAIN-IoT project partners.
- The Eclipse IoT Proposal explaining the benefits in joining an open source community like the Eclipse Foundation and explaining the steps the BRAIN-IoT project proposal has to go through to be sustainable in this open source community.
The purpose of this document is to present the initial Data Management Plan (DMP) of the BRAIN-IoT project and to provide the guidelines for maintaining the DMP during the project.
The Data Management Plan methodology approach adopted for the compilation of D6.1 has been based on the updated version of the “Guidelines on FAIR Data Management in Horizon 2020 version 3.0 released on 26 July 2016 by the European Commission Directorate – General for Research & Innovation”. It defines how data in general and research data in particular will be handled during the research project and will make suggestions for the after-project time. It describes what data will be collected, processed or generated within the scope of the project, what methodologies and standards shall be followed during the collection process, whether and how these data shall be shared and/or made open for the evaluation needs, and how they shall be curated and preserved.
All BRAIN-IoT data will be handled according to EU Data protection and Privacy regulation and the General Data Protection Regulation (GDPR).
Deliverable D3.1 is aiming to design a BRAIN-IoT modelling language IoT-ML which allows to virtualize concrete physical world devices including also complex system such as autonomous robots and critical control devices, as well as data and capabilities models for cross-platform interoperability. The main challenge in this work is to design a IoT Modelling language which can embrace multiple domains. The framework designed could provide the capability to model several different aspects, meaning it could support several individual modelling approaches as well as modelling languages.
This document explores how BRAIN-IoT approaches the requirements of initial device discovery, search, composition and orchestration in a manner that addresses these non-functional challenges. The proposed approach is sufficiently flexible to deal with any foreseeable runtime use case and this document maps out a strategy to deliver this. However, it should also be noted that current BRAIN-IoT Use Case(s) will be subsets of these generalised behaviours.
This document reports on the initial threat modeling and security assessment of the BRAIN-IoT proposed scenarios and the followed security methodology which is based on known threats analyzed by international initiatives undergoing in the EU and worldwide. Starting from the scenarios and architectural solutions defined by WP2, the authors performed an initial analysis considering intentional threats that may result in BRAIN-IoT services to be compromised or disrupted.
This document represents the first iteration of the BRAIN-IoT architecture with focus on: i) identifying BRAIN-IoT things and platforms, ii) defining initial set of requirements, iii) defining initial version of the BRAIN-IoT reference architecture, iv) identifying BRAIN-IoT relevant technologies, v) defining Proof-of-Concept specifications.
The reference architecture and the requirement list in this deliverable will be revised, extended and refined in the next iteration.
Initial version of the vision, application scenarios and use cases in which the results of the BRAIN-IoT project will be demonstrated. D2.1 work has been conducted using domain analysis and brainstorming sessions involving relevant stakeholders and use case analyses. This document reports on the iterative process of ideation which resulted in the definition of: 1) the workflow of the workbench, 2) an initial set of relevant stakeholders, 3) the communication flow between them, and 4) the initial set of use cases.