BRAIN-IoT: Paving the Way for Next-Generation Internet of Things

Enrico Ferrera

, Xu Tao

, Davide Conzon

, Victor Sonora Pombo

, Miquel Cantero

, Tim Ward

Ilaria Bosi

and Mirko Sandretto

LINKS Foundation – Leading Innovation & Knowledge for Society, Turin, Italy

Improving Metrics, A Coruña, Spain

Robotnik Automation S.L.L., Valencia, Spain

Paremus Ltd, Wokingham, U.K.

victorsp@improvingmetrics.com, mcantero@robotnik.es, tim.ward@paremus.com

Keywords: Next-Generation Internet of Things, Self-aware Systems, Semi-autonomous Systems, Security, Privacy,

Edge Computing, Model-based Development, Smart Behaviours.

Abstract: Nowadays, the adoption of the Internet of Things is drastically increasing in different domains and is

contributing to the fast digitalization of several different critical sectors. In the near future, next generation of

IoT-based systems will become more complex to be designed and managed. An opportunity for the

development of flexible smart IoT-based systems that drive the business decision-making is to take more

precise and accurate decisions at the right time, collecting real-time IoT generated data. This involves a set of

challenges including the complexity of IoT-based systems and the management of large-scale systems

scalability. With respect to these challenges, we propose to automate the management of IoT-based systems

mainly based on an autonomic computing approach; these systems should implement cognitive capabilities

that allow them learning and generating decisions at the right time. Consequently, we propose a model-driven

methodology for designing smart IoT-based systems. With this objective, BRAIN-IoT paves the way to

develop and demonstrate novel IoT concepts and solutions to underpin the Next Generation Internet of Things

vision and architecture, that focusing on self-aware and semi-autonomous IoT systems, as well as on moving

away from centralized cloud-computing solutions towards distributed intelligent edge computing systems.

1 INTRODUCTION

Nowadays, the adoption of the Internet of Things

(IoT) is drastically increasing in every application

domain, contributing to the fast digitalization of

contemporary society. Current IoT scenarios are

demonstrating to be constantly increasing in terms of

demanding non-functional requirements, from low

latency to high reliability, and dynamic resources

allocation. This paradigm shift, also considered as the

next evolution phase of IoT (Fettweis, 2014), is

expected to create numerous opportunities for

technology market supporting applications such as

critical infrastructures management, and cooperative

service robotics.

To cope with these demanding requirements, a

multitude of novel technologies - such as Edge

Computing, Artificial Intelligence and Analytics,

Digital Twin, as well as Security, Privacy and Trust

schemes – are being investigated in order to be

adopted in current IoT architectures standards

(Vermesan & Bacquet, 2019), identifying efficient

integration schemes with proper design patterns.

Henceforth, next generation of IoT-based systems are

set to become more complex to design and manage.

Nonetheless, a set of challenges including the

complexity of IoT systems in domains like Smart

Factories and Smart Cities and the management of the

possibly conflicting requirements, interoperability

among distributed heterogeneous technologies and

data, as well as system scalability, requires an

evolving software eco-system which can adapt,

change and scale in response to local requirements

and environmental changes to drive efficiently the

business decision-making. Also, the distributed

nature of IoT makes enforcement of good security

practices intrinsically challenging. The market asks

for IoT solutions suitable to safely support business-

critical tasks, which can be deployed rapidly and with

low costs. Modern IoT applications operating in

470

Ferrera, E., Tao, X., Conzon, D., Pombo, V., Cantero, M., Ward, T., Bosi, I. and Sandretto, M.

BRAIN-IoT: Paving the Way for Next-Generation Internet of Things.

DOI: 10.5220/0009818504700477

In Proceedings of the 5th Inter national Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 470-477

ISBN: 978-989-758-426-8

different scenarios, such as Smart Cities, Industry 4.0,

etc., are complex software ecosystems with strict

requirements of geographic distribution, scalability,

heterogeneity, dynamic evolution, security and

privacy protection, highly more challenging than the

ones required by the traditional (e.g. domotics)

environments. Two of the main challenges arising in

the current Internet of Things scenarios, on one side,

the requirement of designing applications involving

several heterogeneous platforms and smart Things

interconnected to each other in the same environment

and, on the other side, the need to be able to

instantiate, operate and evolve the complex software

ecosystem, reacting automatically and at runtime to

environmental changes, without the human

intervention. With respect to these challenges, which

relates to the complexity of IoT systems management,

the authors propose to automate the management of

IoT systems based on an autonomic computing

approach. However, autonomic computing alone is

not enough for the development of smart IoT-based

systems. Indeed, these systems should implement

cognitive capabilities that allow them learning and

generating decisions at the right time (Kephart &

Chess, 2003). Consequently, we propose a model-

driven methodology for designing smart IoT systems.

With this objective, the BRAIN-IoT project (Brain-

IoT, 2018), funded by the European Commission,

paves the way to develop and demonstrate novel IoT

concepts and solutions to underpin the Next

Generation Internet of Things (NGIoT) vision, which

requires the definition of next generation IoT

architectures focusing on self-aware and semi-

autonomous IoT systems, as well as the migration

from centralized cloud-computing solutions towards

distributed intelligent edge computing systems

(Ferrera, et al., 2018).

While EU-based initiatives are devoting

significant amount of effort to tackle such issues,

often with very positive results, efficient solutions

suitable to tackle challenges arising in NGIoT

scenarios are still missing (NGIoT, 2019). Future

critical issues may be hiding under the hood already

now and be ready to appear in the close future. To be

economically sustainable and achieve solution

longevity, a flexible and dynamic self-managed

system is needed.

The rest of the paper is organized as follows:

Section 2 provides an overview of the background on

autonomic computing and current state of the art of

the application of MAPE loop approach in IoT

domain. Section 3 introduces the BRAIN-IoT

Platform architecture starting from a general,

functional, overview and continuing with objectives

to be accomplished and design choices. Section 4

presents the Service Robotics use-case scenario

where the Platform has been applied and tested.

Finally, Section 5 and Section 6 draw conclusions and

discuss future works to be carried in the next phase of

the project.

2 BACKGROUND AND RELATED

WORKS

One of the most important challenges in self-

adaptation is to create the ability in the system to

reason about itself and its context (Abeywickrama &

Zambonelli, 2012) (Renart, Balouek-Thomert, &

Parashar, 2017).

An autonomic system is composed of many self-

managed components that interact with each other

autonomously, giving some kind of decision-making

mechanism, such as policies, from administrators

(Bueno, 2012). The self-management system must be

continuously monitoring itself to be aware of changes

in the system that might require either reconfiguration

or optimization of the components, protecting itself

against suspected wrong behaviour or recovering

from failures (Villegas & Müller, 2011): BRAIN-IoT

focuses on these last two aspects. Adaptation refers to

the capability of changing the software structure or

behaviour according to significant alterations in the

environment. This adaptation must occur

dynamically and at runtime.

The MAPE loop (Monitor, Analyzer, Planner, and

Executer) (Lee, Seo, & Kim, 2019) is a crucial

feedback for the implementation loop of self-adaptive

software and autonomic computing. These four parts

communicate and collaborate to each other and

exchange appropriate knowledge and information.

The Monitor collects the information from the

software and its environment, the Analyzer observes

the reported situation and determines if any change

needs to be made, the Planner creates the plan to

achieve the changes, and the Executer provides the

mechanism to perform the changes over the managed

system. Various decentralized MAPE loop patterns

have been studied for self-adaptive software such as

coordinated control, information sharing, master–

slave, regional planning, and hierarchical control.

The following paragraph will report the various

IoT studies conducted on self-adaptive software, with

a focus on its application in distributed systems and

IoT domain. One of the first approaches related to a

self-adaptive distributed decision support model is

proposed by Zhang (Zhang, Alharbe, & Atkins, 2016)

BRAIN-IoT: Paving the Way for Next-Generation Internet of Things

471

with a simulated inventory management system for a

warehousing company. Ouechtati (Ouechtati,

Azzouna, & Said, 2018) presented a middleware for

IoT, which can adapt process of access control rules

that satisfy the requirements of IoT environments. A

designed MAPE loop-based management

architecture patterns for an adaptation system in IoT

environments was designed by Ribeiro (Ribeiro, de

Almeida, Moreno, & Montesco, 2016). Muccini

(Muccini, Spalazzese, Moghaddam, & Sharaf, 2018)

surveyed IoT distribution patterns and self-

adaptation, and simulated with an IoT-based forest

monitoring system based on the MAPE loop. In the

health area (Azimi, et al., 2017) presented a MAPE

loop with shared knowledge (MAPE-K) based on

hierarchical computing architecture (HiCH) for IoT-

based health monitoring systems. Welsh (Welsh,

Bencomo, Sawyer, & Whittle, 2014) implemented a

self-adaptive system with goal-based requirement

models to ensure self-explanation behaviours at

runtime. In order to simplify the engineering and

coordination of services in dynamic IoT

environments in (Beal, Pianini, & Viroli, 2015) the

aggregate programming (focuses on ensuring the

simplified design, creation, and maintenance of IoT

systems) was employed and then demonstrated in the

Alchemist simulation (Pianini, Montagna, & Viroli,

2013), which is an extensible meta simulator for

pervasive computing. In (Bucchiarone, Marconi,

Pistore, & Raik, 2017) was promoted a framework

with runtime service composition in a dynamic

context, using a service model with stateful, non-

deterministic, and asynchronous features. Renart

(Renart, Diaz-Montes, & Parashar, 2017) proposed a

framework to support dynamic data driven IoT

applications called Pulsar, that leverages edge

resources to support location and content aware

processing of data streams. Also, a self-adaptive

framework for reliable multiple autonomic loops was

proposed by (Sylla, Louvel, Rutten, & Delaval,

2017).

Furthermore, a new challenge related on self-

adaptive software concerns the complexity that arises

from the wealth of information that can be associated

with runtime phenomena. Trying to extend the

applicability of models produced in Model-Driven

Engineering (MDE) approaches to the runtime

environment, the research on models@runtime

focuses on providing effective technologies for

managing the complexity of evolving software

behaviour while it is executing (Bencomo, Götz, &

Song, 2019). A models@runtime is a causally

connected self-representation of the associated

system, so it is possible to use these to support

dynamic state monitoring and control of systems

during execution (runtime behaviour observation), to

support design errors fixing or even controlled

ongoing design. In other words, in order to monitor

and validate the correct execution of the platform,

models@runtime could provide a real-time picture of

the status of the system, synchronizing its internal

status with its model (Blair, Bencomo, & France,

2009). This guarantees the system operators to

constantly have a clear view of the situation, checking

whether the system is behaving as expected, and

giving the possibility to promptly react in case of

design issues.

3 BRAIN-IoT PLATFORM

3.1 Overview

In this paper, the authors focus on IoT environments

consisting of different sensors, actuators, external

services, and requirements that should be

dynamically satisfied at runtime. To achieve this, a

self-adaptive software framework is proposed for an

IoT environment with two phases: Modelling &

Validation, and Execution. In Figure 1, presents an

overview of the proposed platform.



Figure 1: Overview of BRAIN-IoT Platform.

The Modelling & Validation Framework is

responsible for designing the logic of the Application

which is going to be constructed based on the actions

and relations between the available devices (i.e.

sensors, actuators, Cyber-Physical Systems - CPSs)

and external services (e.g. weather forecast, open

data, third-party IoT platforms, databases). The

Application logic is modelled along with the relevant

IoT environment as a Finite-State Machine (FSM)

describing its self-adaptive behaviour. Finally, the

abstracted FSM is converted in source code which is

deployed and executed by the Execution Platform.

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The Execution Platform implements a MAPE

loop, which make the Platform dynamic for runtime

adaptation. Leveraging on a library of deployable

microservices, the Execution Platform allows the

instantiation of an Interoperability Layer and an

Autonomic Layer. The Interoperability Layer is

composed by the set of microservices which are

responsible for the communication and semantic

adaptation to the available IoT Devices and/or

External Services, hence performing the Monitoring

of the environment and collection of data, and

Execution of specific actions to implement the

decision taken from the Autonomic Layer. The

Autonomic Layer is composed by the set of Smart

Behaviours microservices which are responsible for

Analysing data and consequently Planning the set of

actions for satisfying the requirements of the

modelled application logic. The Modelling &

Validation Framework offers the ability to use

development-time models to supervise running

Execution Platform states. This solution enables

monitoring of microservices states and starting and

stopping them from the Modelling & Validation

Framework. BRAIN-IoT Platform is also able to use

models@runtime to modify the system’s behavior at

runtime in response to changes within the system.

3.2 Objectives

To address the challenges outlined in Section 1,

BRAIN-IoT platform aims to support IoT

professionals to reducing the effort for designing,

developing, deploy, configure, optimize, and

maintain IoT Systems based on existing and new IoT

Services. BRAIN-IoT simplify the process of

implementing and deploying code into production,

i.e. scaling, capacity planning and maintenance

operations may be hidden from the developer or

operator. To pursue this ambition, BRAIN-IoT aims

to achieve the following four macro-objectives,

focusing on two sub-objectives each.

I. Facilitates the specification and design of

complex IoT systems.

A. Defining a Domain-Specific Modelling

Language for IoT and CPS service composition

enabling the creation of mashups of existing and

new IoT services communicating with different

protocols.

B. Specifying the data-flow mappings

between the different IoT service interfaces in a

seamless way, as well as the behaviour logic

implementing the orchestration of such services.

II. Enabling self-adaptive deployment and

management of distributed IoT systems.

A. Developing a dynamic Cloud/Edge runtime

infrastructure which simplifies and automates the

process of deploying and managing distributed

IoT applications, allowing the automatic

installation and replacement of smart behaviours,

as well as semantic and syntactic adapters for IoT

Devices and Services, reacting to environmental

changes and User events.

B. Adopting advanced self-learning features,

realized by means of modular Artificial

Intelligence algorithms, which can recognize non-

critical and critical events (such as data stream

anomalies) to trigger the reconfiguration (such as

new behaviors deployment, self-healing

capabilities, etc.) of the runtime infrastructure.

III. Enforcing Security and Privacy.

A. Establishing Authentication, Authorization

and Accounting (AAA) in dynamic, distributed

IoT scenarios.

B. Providing solutions to embed privacy-

awareness and privacy control features in IoT

solutions.

IV. Complex IoT systems validation and safety

enforcement.

A. Developing IoT devices models for

evaluating the system in a safe environment

before executing it in the real environment.

B. Fast prototyping and supervising of critical

running systems, via real-time models, to validate

them before instantiating in the real environment.

3.3 Architecture

To achieve its objectives, BRAIN-IoT project

develops a Platform which consists of four main

technological categories:

Modelling & Validation Framework. Defines a

domain-specific Modelling Language describing IoT

devices capabilities and system-level behaviours;

provides toolset supporting the syntax of the

modelling language, allowing model verification and

model checking, and automatic code generation to

provide rapid model-based development approach.

Execution platform. Provides an autonomic

distributed infrastructure for the dynamic deployment

and execution of smart behaviours and IoT devices

and external services adapters.

Security & Privacy Framework. Provides security

and privacy awareness and protection throughout the

BRAIN-IoT platform, including end-to-end security

service.

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473

Smart Behaviours. Enable autonomic

functionalities for the Runtime Infrastructure through

the deployment and execution of reusable software

AI/ML algorithms.

The four technological categories are made of

several software components, or “Products”, which

compose the overall BRAIN-IoT Platform, as

represented in

Figure

In BRAIN-IoT Modelling & Validation

Framework a new meta-language, namely the

BRAIN-IoT Modelling Language (BRAIN-IoT-ML)

is defined. BRAIN-IoT-ML is a UML profile, which

extends the generic UML for the IoT domain.

BRAIN-IoT-ML implements the concepts in the IoT-

A (De Loof, et al., 2013) domain model.

Figure 2: BRAIN-IoT Platform Architecture.

BRAIN-IoT-ML modelling approach is

component-based and behaviours are described as

state-machines. As a UML profile, BRAIN-IoT-ML

integrates well with other UML profiles, e.g.,

MARTE, SysML, and AI Module profiles to be

developed within the project.

IoT-ML aims at proposing a system-level

description of functional models. The goal is to add

IoT domain concepts in this kind of model, at this

level of abstraction. AI concepts shall also

complement the IoT models and allow syntactical and

semantical compatibility analysis between AI

modules.

BRAIN-IoT-ML and AI Module Modelling

Language have a dedicated modelling tool to apply

Model-Based Engineering. BRAIN-IoT-ML

modelling tool uses a Model-Based Engineering

(MBE) approach to help component-based

modelling, facilitating the linking toward real devices

and external services through meta-data

representation in WoT TD (W3C, 2020), monitor IoT

state-machine based behaviours in a human-friendly

graphical manner through the models@runtime

approach, and finally quickly prototyping before

deployment in the real context to check the goodness

of the designed state machine – leveraging on the

human friendly graphical monitoring of the state

machine itself, by generating the source code for

monitoring and controlling the BRAIN-IoT

adaptation microservices. The tool also validates

syntactical and semantic compatibility between AI

modules assembled together.

Since the goal of BRAIN-IoT Platform is not to

re-implement yet another IoT middleware but to

facilitate the management of complex IoT systems,

the BRAIN-IoT adaptation microservices have not

been implemented from scratch; counterwise a

mature existing IoT platform such as Eclipse

sensiNact (Eclipse, 2020) has been integrated to cope

with the interoperability issues.

Eclipse sensiNact consists of a software platform

enabling the collection, processing and redistribution

of any data relevant to improving the quality of life of

urban citizens, programming interfaces allowing

different modes of access to data (on-demand,

periodic, historic, etc.). Eclipse sensiNact IoT

devices, legacy systems, mobile applications, open

data repositories are the potential exploitable data

sources: sensiNact provides connectivity support to

those data sources including IoT protocols and

platforms such as LoRa, Zigbee, IEEE 802.15.4,

Sigfox, enOcean, MQTT, XMPP, NGSI, HTTP,

CoAP, etc.

The fully modular BRAIN-IoT Execution

Platform is designed not only to be autonomous in its

own right, in that it will be entirely self-managing,

and self-healing but also it will facilitate the

deployment of autonomous microservices,

irrespective of the functionality of that microservice.

Microservices have the capability of dynamic

deployment throughout the framework. In a use case

with variable and unpredictable numbers and

locations of active end devices, this ability is not

available today. Any new edge device on the

framework, irrespective of location, will be able to

dynamically load microservices as required, given the

appropriate security permissions (Nicholson, et al.,

2019). Similarly, reconfiguration of the

microservices will be possible whenever needed. The

current state of the art requires that the overall system

or application is stopped, a configuration pushed out

to the edge devices and the system is then restarted.

The BRAIN-IoT Platform will permit dynamic, “on

the fly” reconfiguration and upgrade without

necessarily halting the total system. Due to the

modularity of the framework and its components,

microservices available to the framework can be

reused or repurposed for other applications with

minimal effort, cutting down both the cost and

implementation time of new applications.

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Along with the support of Smart Behaviours

functionalities, implemented as AI/ML algorithms,

the BRAIN-IoT Platform includes the ability for

designed applications running on the platform to self-

optimise. This is achieved by embedding the ability

to monitor the surrounding environment and the

operating context via specific adaptation

microservices, and then to make analysis and

planning with machine learning (ML) to determine

the best possible configuration of microservices for

achieving the application objectives and

requirements. Autonomous dynamic deployment

built into the BRAIN-IoT Platform means that

microservices can, if deemed appropriate by such an

intelligent management agent, be relocated within the

available infrastructure. This totally autonomous

capability within the BRAIN-IoT Platform represents

a significant advance on current state of the art in IoT

implementations, paving the way for the Next-

Generation IoT paradigm.

4 SERVICE ROBOTICS CASE

STUDY

The overall depicted concept draws requirements and

challenging use cases from IoT applications in a

Service Robotics usage scenarios, which provide the

suitable setting to reflect future challenges in terms of

dependability, need for smart behaviour, and security,

which are expected to become more significant and

impacting in the long-term (10+years).

The Service Robotics use-case involves several

robotic platforms, like the open-source Robotics

Operating System (ROS), which need to collaborate

to scan a given warehouse and to assist humans in a

logistics domain.

The use-case identified for testing the BRAIN-

IoT Platform consists in five robots moving

autonomously within the warehouse, with the goal of

picking carts from a loading area and moving them to

an unloading area. While performing this task,

modelled via BRAIN-IoT-ML and deployed as

software artefacts onto the different robots, the robots

may run into several issues, such as system failures,

battery depletion or cyber-attacks.

The role of BRAIN-IoT Platform is to enforce a

self-healing behaviour which will guarantee the

system to accomplish the full execution of the task.

This is achieved implementing a reusable and generic

component that intends to abstract the

implementation of anomaly detection of a specific

problem to generalize it to problems of the same type.

Aims to identify the anomalies over several data

sources, that can be due to a change in the behaviour

of the sensor, a failure or a miscalibration. It is

necessary to specify the main data sources where the

detection of anomalies shall be done and a set of co-

variables, if any. The Communication between

adaptation microservices – gathering data from data

sources – and such Smart Behaviour microservice is

implemented through the BRAIN-IoT Event Bus. The

Brain IoT Event Bus is a lightweight, asynchronous

eventing system designed to allow communication

between decoupled resources and software

components. It provides type safe access to data, and

basic schema transformation. This allows

components to interact even if their data schema is not

known until compile time.

The event bus is also responsible for interacting

with various security components to validate the

integrity and origin of data as it flows through the

system. This includes the use of the authorisation

engine to limit the sending and delivery of event data.

An analysis of the input data is carried out. It

attempts to identify the main characteristics of each

of the data sources and the correlation between the

different sources. According to the analysis, the

variables to be modelled are selected and a set of

complementary methods is executed, which by

different techniques, extract different types of

anomalies. This anomaly detection is translated in an

event which triggers the instantiation of the same

logic to a new robotic platform which was not part of

the original set of robots and can substitute the robots

which presents the detected anomaly. The Execution

Platform provides automated distributed management

of microservices placement, based upon the concept

of “unhandled events” and the BRAIN-IoT artefact

repository. Microservices and behaviours are

dynamically “resolved” by a Behaviour Management

Service, implemented within the Execution Platform,

on the target node to validate that their dependencies

can be satisfied, and that the node is capable of

running them. Such Behaviour Management Service

takes the role of an OSGi Management Agent (a role

identified, but not specified, by the OSGi Alliance)

and is responsible for the deployment and

management of OSGi bundles and configurations in

the runtime. As such it makes use of related OSGi

specifications such as the Resolver Service and the

OSGi Repository Service. The concepts used by the

Behaviour Management Service are relatively

common in OSGi, although the use of the resolver

service to determine deployments at runtime is used

less frequently. The primary innovation is the use of

live event data from the event bus to dynamically

BRAIN-IoT: Paving the Way for Next-Generation Internet of Things

475

trigger the installation of microservices and

behaviours.

5 CONCLUSIONS

This paper has presented the BRAIN-IoT platform,

explaining how this solution paves the way for the

development and demonstration of novel IoT

concepts and solutions, supporting the Next

Generation Internet of Things vision. In the next

years, such vision, will require the introduction of

next generation IoT architectures able to

autonomously react to system issues and adopt self-

healing and self-protection techniques to guarantee

the reliability and service continuity of complex

distributed IoT systems.

The BRAIN-IoT project provides a platform that

aims to be considered as a reference solution for the

development of applications involving autonomic

computing and distributed IoT systems. The solution

will provide both a Modelling and Validation

Framework that allow designing applications using a

modern model-based design approach and an

Execution Platform, which implements a MAPE

loop. The main features provided by this platform in

terms of dynamicity and self-* capabilities are

possible thanks to the use of the OSGi-based

technologies and more specifically the Requirements

and Capabilities specification, which allows to handle

the autonomous dynamicity of the platform as an

automatic dependencies matchmaking. Leveraging

such technology, the BRAIN-IoT delivers a software

eco-system capable of deploying/assembling,

orchestrating and managing sophisticated IoT

applications. Conceptually, like, but significantly

more flexible than, compute lambdas; BRAIN-IoT

demonstrates how sophisticated distributed

behavioural pipelines of software components can be

dynamically assembled, in response to environmental

triggers. Meanwhile, by pursuing a modular structural

hierarchy (i.e., an holonic approach), BRAIN-IoT

enables the natural creation of federated distributed

environments ideally suited to the demands of several

domains of the modern society.

6 FUTURE WORKS

The BRAIN-IoT project use cases demonstrate the

flexibility of the proposed approach within a diverse

set of operational environments. Specifically, this

paper has focused on the Service Robotics use case,

which demonstrates the use of BRAIN-IoT platform

in a highly automated, dynamic and adaptive

manufacturing environment. Furthermore, the same

approach will be applied also in the second use case

concerning the monitoring and control of public water

management infrastructure for the city of A Coruna,

which will demonstrate the use of BRAIN-IoT in

critical infrastructures and mission-critical

environments.

Finally, during the final phase of the BRAIN-IoT

project, key BRAIN-IoT concepts will be finalized

with regards with their OSGi Alliance specification

process (OSGi Github, 2019).

ACKNOWLEDGEMENTS

This work was supported by the project “BRAIN-

IoT” (H2020-IOT-03-2017, grant agreement no.

780089).

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