Especificación y desarrollo de mecanismos de interoperabilidad a nivel de Middleware y Aplicaciones/Servicios entre Plataformas Heterogéneas de Internet de las Cosas

[ES] El interés en la industria y a nivel académico en el desarrollo en el campo de Internet de las Cosas (IoT) es muy alto. Se han diseñado e implementado una gran cantidad de soluciones a diferentes niveles. Desde soluciones a nivel de dispositivo hasta plataformas IoT completas. No obstante, desarrollar nuevas soluciones IoT en muchos casos puede suponer un esfuerzo complejo. Esta no es una tarea que se deba realizar desde cero. Las plataformas IoT ofrecen las herramientas necesarias para administrar y trabajar con los dispositivos y objetos conectados a ellas. Las plataformas utilizan estos datos para producir resultados y ofrecer servicios y aplicaciones. El ecosistema IoT abarca una amplia gama de dispositivos, sensores, actuadores, entidades de conocimientos, protocolos, tecnologías, redes, plataformas, servicios, aplicaciones, sistemas y datos muy diversos. Como consecuencia de su naturaleza heterogénea y la ausencia de un estándar global de IoT, un hecho que tampoco se espera lograr en un futuro próximo, en lugar de lograr la integración perfecta entre los diferentes sistemas IoT proliferan diferentes tecnologías y sistemas que implementan sus propios protocolos de interoperabilidad para los objetos que componen IoT. El trabajo realizado en esta Tesis Doctoral se encarga de revertir esta problemática asociada a la heterogeneidad de las plataformas IoT y la falta de un estándar de interoperabilidad predominante en el mercado. Por tanto, el objetivo de la misma es ofrecer una solución centrada en aprovechar las diferentes ventajas que ofrecen las plataformas, aplicaciones y servicios IoT disponibles, para ofrecer una serie de mecanismos de interoperabilidad y un marco común que permitan poder acceder, interactuar e intercambiar información y funcionalidades entre las diferentes plataformas IoT. Concretamente, la Tesis Doctoral se centra en las necesidades de interoperabilidad de plataformas IoT en las capas de Middleware y Aplicación y Servicios. Desde la perspectiva de los mecanismos de la capa middleware, la Tesis Doctoral establece soluciones basadas en una capa de abstracción que facilita el acoplamiento de las diferentes plataformas. Esto proporciona funcionalidades para acceder a las principales características e información de las diferentes plataformas IoT. Desde la perspectiva de los mecanismos de la capa de aplicación y servicios, se diseñan y definen soluciones para el acceso común y la interacción entre los distintos servicios y aplicaciones heterogéneos ofrecidos por las plataformas. Además, en la Tesis Doctoral se presentan aquellos elementos transversales para ofrecer una solución de interoperabilidad completa. En primer lugar, se exponen aquellos requisitos necesarios para gestionar la confianza, seguridad, privacidad, virtualización, extensibilidad o escalabilidad. En segundo lugar, se presenta la definición de un marco común de interoperabilidad que proporciona una forma de unificar los diferentes mecanismos de interoperabilidad presentados. También se ofrecen herramientas para gestionar, acceder y hacer un uso adecuado de los mecanismos de interoperabilidad. Finalmente, se presenta la aproximación a la solución propuesta llevada a cabo en los proyectos europeos H2020: INTER-IoT, ACTIVAGE, PIXEL y DataPorts. Estos proyectos han servido para definir, desarrollar y validar los mecanismos de interoperabilidad y la solución presentada en esta Tesis Doctoral.[CAT] L'interés en la indústria i a nivell acadèmic en el desenvolupament en el camp d'Internet de les Coses (IoT) és molt alt. S'han dissenyat i implementat una gran quantitat de solucions a diferents nivells. Des de solucions a nivell de dispositiu fins a plataformes IoT completes. No obstant això, desenvolupar noves solucions IoT en molts casos pot suposar un esforç complex. Aquesta no és una tasca que s'haja de realitzar des de zero. Les plataformes IoT ofereixen les eines necessàries per a administrar i treballar amb els dispositius i objectes connectats a elles. Les plataformes utilitzen aquestes dades per a produir resultats i oferir serveis i aplicacions. L'ecosistema IoT es compon d'una una àmplia gamma de dispositius, sensors, actuadors, entitats de coneixements, protocols, tecnologies, xarxes, plataformes, serveis, aplicacions, sistemes i dades molt diverses. A conseqüència de la seua naturalesa heterogènia i l'absència d'un estàndard global de IoT, un fet que tampoc s'espera aconseguir en un futur pròxim, es produeix que en lloc d'aconseguir la integració perfecta entre els diferents sistemes IoT, proliferen diferents tecnologies i sistemes que implementen els seus propis protocols d'interoperabilitat per als objectes que componen Internet de les Coses. El treball realitzat en aquesta tesi doctoral s'encarrega de revertir aquesta problemàtica associada a l'heterogeneïtat de les plataformes IoT i la falta d'un estàndard d'interoperabilitat predominant en el mercat. Per tant, l'objectiu és oferir una solució centrada en aprofitar els diferents avantatges que ofereixen les plataformes, aplicacions i serveis IoT disponibles, per a oferir una sèrie de mecanismes d'interoperabilitat i un marc comú que permeten poder accedir, interactuar i intercanviar informació i funcionalitats entre les diferents plataformes IoT. Concretament, el treball se centra en les necessitats d'interoperabilitat de plataformes IoT en les capes de Middleware i Aplicació i Serveis. Des de la perspectiva dels mecanismes de la capa Middleware, el present treball estableix solucions basades en una capa d'abstracció que facilita la unificació de les diferents plataformes. Això proporciona les funcionalitats per a accedir a les principals les característiques i informació de les diferents plataformes IoT. Des de la perspectiva dels mecanismes de la capa d'aplicació i serveis, es dissenya i defineixen solucions per a l'accés comú i la interacció entre els diferents serveis i aplicacions heterogenis oferits per les plataformes. A més, es presenten en el present treball aquells elements transversals per a oferir una solució d'interoperabilitat completa. En primer lloc, aquells requisits necessaris per a gestionar la confiança, seguretat, privacitat, virtualització, extensibilitat o escalabilitat. En segon lloc, la definició d'un marc comú d'interoperabilitat que proporciona una manera d'unificar els diferents mecanismes d'interoperabilitat presentats. Oferint eines per a gestionar, accedir i fer un ús adequat dels mecanismes d'interoperabilitat. Finalment, es presenta l'aproximació a la solució proposada duta a terme en els projectes europeus H2020: INTER-IoT, ACTIVAGE, PÍXEL i DataPorts. Aquests projectes han servit per a definir, desenvolupar i validar els mecanismes d'interoperabilitat i la solució oferida en aquesta tesi doctoral.[EN] There is a strong interest in the field of the Internet of Things (IoT) in the industry and the academia. A large number of solutions have been designed and implemented at different levels. From device level solutions to complete IoT platforms. However, developing new IoT solutions can be a challenging task. This is not a task that needs to be done from scratch. IoT platforms provide the tools needed to manage and access to the devices and objects connected to them. The platforms can take advantage of this data to produce results and deliver services and applications. The IoT ecosystem encompasses a wide range of diverse devices, sensors, actuators, knowledge entities, protocols, technologies, networks, platforms, services, applications, systems and data. As a consequence of its heterogeneous nature and the absence of a global IoT standard, something that is also not expected to be achieved soon, instead of achieving seamless integration between different IoT systems, different technologies and systems proliferate and providing their own interoperability protocols for the objects related with Internet of Things. The work carried out in this PhD thesis aims to address this problem associated with the heterogeneity of IoT platforms and the lack of a predominant interoperability standard in the market. Therefore, the objective is to offer a solution focused on taking advantage of the different benefits offered by the available IoT platforms, applications and services, in order to offer a series of interoperability mechanisms and a common framework that allows accessing, interacting and exchanging information and functionalities between the different IoT platforms. Specifically, the work is focused on the interoperability needs at the Middleware and Application and Services layers of the IoT Platforms. From the perspective of the Middleware layer mechanisms, this work establishes solutions based on an abstraction layer that facilitates the coupling of the different platforms. This provides functionalities to access to the main features and information of the different IoT platforms. From the perspective of the Application and Service layer mechanisms, this work designs and defines solutions for common access and interaction between the different heterogeneous services and applications offered by the IoT platforms. In addition, this PhD tesis presents those cross-cutting aspects needed to provide a complete interoperability solution. Firstly, those requirements involved in to manage trust, security, privacy, virtualisation, extensibility or scalability. Secondly, the definition of a common interoperability framework that provides a way to unify the different interoperability mechanisms presented. It offers tools for managing, accessing and making appropriate use of the interoperability mechanisms developed in this work. Finally, it describes the approach to the proposed solution carried out in the following H2020 european projects: INTER-IoT, ACTIVAGE, PIXEL and DataPorts. These research projects have been used to define, develop and validate the interoperability mechanisms and the solution offered in this PhD tesis.Belsa Pellicer, A. (2022). Especificación y desarrollo de mecanismos de interoperabilidad a nivel de Middleware y Aplicaciones/Servicios entre Plataformas Heterogéneas de Internet de las Cosas [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185508TESI

Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case

[EN] During the past few decades, the combination of flourishing maritime commerce and urban population increases has made port-cities face several challenges. Smart Port-Cities of the future will take advantage of the newest IoT technologies to tackle those challenges in a joint fashion from both the city and port side. A specific matter of interest in this work is how to obtain reliable, measurable indicators to establish port-city policies for mutual benefit. This paper proposes an IoTbased software framework, accompanied with a methodology for defining, calculating, and predicting composite indicators that represent real-world phenomena in the context of a Smart PortCity. This paper envisions, develops, and deploys the framework on a real use-case as a practice experiment. The experiment consists of deploying a composite index for monitoring traffic congestion at the port-city interface in Thessaloniki (Greece). Results were aligned with the expectations, validated through nine scenarios, concluding with delivery of a useful tool for interested actors at Smart Port-Cities to work over and build policies upon.This research was funded, by the European Commission, via the agency INEA, under the H2020-project PIXEL, grant number 769355, and, when applicable, by the H2020-project DataPorts, grant number 871493, via the DG-CONNECT agency.Lacalle, I.; Belsa, A.; Vaño, R.; Palau Salvador, CE. (2020). Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case. Sensors. 20(15):1-41. https://doi.org/10.3390/s20154131S1412015Urban Population Growthhttps://www.who.int/gho/urban_health/situation_trends/urban_population_growth_text/en/Smart Port Cityhttps://maritimestreet.fr/smart-port-city/The World’s 33 Megacitieshttps://www.msn.com/en-us/money/realestate/the-worlds-33-megacities/ar-BBUaR3vDocksTheFuture Project Maritime Traffic Analysis and Forecast Review-Key Resultshttps://www.docksthefuture.eu/wp-content/uploads/2020/04/Attachment_0-2019-09-09T135818.886-1.pdfHamburg Port Authority: SmartPORThttps://www.hamburg-port-authority.de/en/hpa-360/smartport/Guo, H., Wang, L., Chen, F., & Liang, D. (2014). Scientific big data and Digital Earth. Chinese Science Bulletin, 59(35), 5066-5073. doi:10.1007/s11434-014-0645-3AIVP Agenda 2030 for Sustainable Port-Citieshttps://www.aivpagenda2030.com/Urban Transport Challengeshttps://transportgeography.org/?page_id=4621Passenger Cars in the EUhttps://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EUAverage CO2 Emissions from New Cars and Vans Registered in Europe Increased in 2018, Requiring Significant Emission Reductions to Meet the 2020 Targetshttps://ec.europa.eu/clima/news/average-co2-emissions-new-cars-and-vans-registered-europe-increased-2018-requiring-significant_en7 Smart City Solutions to Reduce Traffic Congestionhttps://www.geotab.com/blog/reduce-traffic-congestion/The Port and the City—Thoughts on the Relation between Cities and Portshttps://theportandthecity.wordpress.com/Yau, K.-L. A., Peng, S., Qadir, J., Low, Y.-C., & Ling, M. H. (2020). Towards Smart Port Infrastructures: Enhancing Port Activities Using Information and Communications Technology. IEEE Access, 8, 83387-83404. doi:10.1109/access.2020.2990961Two Projects Led by Valenciaport Win the IAPH World Port Sustainability Awards 2020—Valenciaporthttps://www.valenciaport.com/en/two-projects-led-by-valenciaport-win-the-iaph-world-port-sustainability-awards-2020/Ahlgren, B., Hidell, M., & Ngai, E. C.-H. (2016). Internet of Things for Smart Cities: Interoperability and Open Data. IEEE Internet Computing, 20(6), 52-56. doi:10.1109/mic.2016.124Inkinen, T., Helminen, R., & Saarikoski, J. (2019). Port Digitalization with Open Data: Challenges, Opportunities, and Integrations. Journal of Open Innovation: Technology, Market, and Complexity, 5(2), 30. doi:10.3390/joitmc5020030Analytical Report 4: Open Datain Citieshttps://www.europeandataportal.eu/sites/default/files/edp_analytical_report_n4_-_open_data_in_cities_v1.0_final.pdfAnalytical Report 6: Open Datain Cities 2https://www.europeandataportal.eu/sites/default/files/edp_analytical_report_n6_-_open_data_in_cities_2_-_final-clean.pdfINTER-IoT Deliverableshttps://inter-iot.eu/deliverablesActivage Project D3.1 Report on IoT European Platformshttps://www.activageproject.eu/docs/downloads/activage_public_deliverables/ACTIVAGE_D3.1_M3_ReportonIoTEuropeanPlatforms_V1.0.pdfThe Open Source Platform for Our Smart Digital Future—FIWAREhttps://www.fiware.org/FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/index.htmlApache Kafkahttps://kafka.apache.org/FIWARE Orion Context Brokerhttps://fiware-orion.readthedocs.io/en/master/Saborido, R., & Alba, E. (2020). Software systems from smart city vendors. Cities, 101, 102690. doi:10.1016/j.cities.2020.102690Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., & Milojicic, D. S. (2018). Software Platforms for Smart Cities. ACM Computing Surveys, 50(6), 1-37. doi:10.1145/3124391Smart Citieshttps://www.fiware.org/community/smart-cities/Araujo, V., Mitra, K., Saguna, S., & Åhlund, C. (2019). Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities. Journal of Parallel and Distributed Computing, 132, 250-261. doi:10.1016/j.jpdc.2018.12.010Ismagilova, E., Hughes, L., Dwivedi, Y. K., & Raman, K. R. (2019). Smart cities: Advances in research—An information systems perspective. International Journal of Information Management, 47, 88-100. doi:10.1016/j.ijinfomgt.2019.01.004Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Alavi, A. H., Jiao, P., Buttlar, W. G., & Lajnef, N. (2018). Internet of Things-enabled smart cities: State-of-the-art and future trends. Measurement, 129, 589-606. doi:10.1016/j.measurement.2018.07.067Samih, H. (2019). Smart cities and internet of things. Journal of Information Technology Case and Application Research, 21(1), 3-12. doi:10.1080/15228053.2019.1587572Lanza, J., Sánchez, L., Gutiérrez, V., Galache, J., Santana, J., Sotres, P., & Muñoz, L. (2016). Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case. Energies, 9(9), 719. doi:10.3390/en9090719A Novel Architecture for Modelling, Virtualising and Managing the Energy Consumption of Household Appliances|AIM Project|FP7|CORDIS|European Commissionhttps://cordis.europa.eu/project/id/224621Intelligent Use of Buildings’ Energy Information|IntUBE Project|FP7|CORDIS|European Commissionhttps://cordis.europa.eu/project/id/224286Scuotto, V., Ferraris, A., & Bresciani, S. (2016). Internet of Things: applications and challenges in smart cities. A case study of IBM smart city projects. Business Process Management Journal, 22(2). doi:10.1108/bpmj-05-2015-0074Molavi, A., Lim, G. J., & Race, B. (2019). A framework for building a smart port and smart port index. International Journal of Sustainable Transportation, 14(9), 686-700. doi:10.1080/15568318.2019.1610919Moustaka, V., Vakali, A., & Anthopoulos, L. G. (2019). A Systematic Review for Smart City Data Analytics. ACM Computing Surveys, 51(5), 1-41. doi:10.1145/3239566Alam, M., Dupras, J., & Messier, C. (2016). A framework towards a composite indicator for urban ecosystem services. Ecological Indicators, 60, 38-44. doi:10.1016/j.ecolind.2015.05.035PIXEL Project D5.1 Environmental Factors and Mapping to Pilotshttps://pixel-ports.eu/wp-content/uploads/2020/05/D5.1-Environmental-aspects-and-mapping-to-pilots.pdfEconomic Sentiment Indicator—Eurostathttps://ec.europa.eu/eurostat/web/products-datasets/product?code=teibs010Human Development Index (HDI)|Human Development Reportshttp://hdr.undp.org/en/content/human-development-index-hdiCOIN|Competence Centre on Composite Indicators and Scoreboardshttps://composite-indicators.jrc.ec.europa.eu/CITYkeys Projecthttp://www.citykeys-project.eu/citykeys/homeCITYkeys D1-4 Indicators for Smart City Projects and Smart Citieshttp://nws.eurocities.eu/MediaShell/media/CITYkeysD14Indicatorsforsmartcityprojectsandsmartcities.pdfMake Healthy Choices Easier Options—Scientific Americanhttps://www.scientificamerican.com/podcast/episode/make-healthy-choices-easier-options-12-09-20/FIWARE E Interoperabilidad Para Smart Citieshttps://www.apegr.org/images/descargas/J7OctESMARTCITY/2PresentacionFIWARE.pdfChen, G., Govindan, K., & Yang, Z. (2013). Managing truck arrivals with time windows to alleviate gate congestion at container terminals. International Journal of Production Economics, 141(1), 179-188. doi:10.1016/j.ijpe.2012.03.033Patel, N., & Mukherjee, A. B. (2015). Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index. Bulletin of Geography. Socio-economic Series, 30(30), 123-134. doi:10.1515/bog-2015-0039Aimsun Live: Model Every Movement at Every Momenthttps://www.aimsun.com/aimsun-live/PTV Vissim: Traffic Simulation Softwarehttps://www.ptvgroup.com/en/solutions/products/ptv-vissim/IBM Traffic Prediction Toolhttps://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=1248Veins: The Open Source Vehicular Network Simulation Frameworkhttps://veins.car2x.org/Mena-Yedra, R., Gavaldà, R., & Casas, J. (2017). Adarules: Learning rules for real-time road-traffic prediction. Transportation Research Procedia, 27, 11-18. doi:10.1016/j.trpro.2017.12.106PIXEL Projecthttps://pixel-ports.euReference Architectural Model Industrie 4.0 (rami 4.0)https://www.plattform-i40.de/PI40/Navigation/EN/Home/home.htmlSethi, P., & Sarangi, S. R. (2017). Internet of Things: Architectures, Protocols, and Applications. Journal of Electrical and Computer Engineering, 2017, 1-25. doi:10.1155/2017/9324035Containers & Containerization—The Pros and Conshttps://spin.atomicobject.com/2019/05/24/containerization-pros-cons/Pyngsi Frameworkhttps://github.com/pixel-ports/pyngsiPIXEL Project D6.2 PIXEL Information System Architecture and Design—Version 2https://pixel-ports.eu/wp-content/uploads/2020/05/D6.2-PIXEL-Information-System-architecture-and-design-v2.pdfApache Hivehttps://hive.apache.org/MySQLhttps://www.mysql.com/MariaDB Serverhttps://mariadb.org/Elasticsearchhttps://www.elastic.co/elasticsearch/MongoDBhttps://www.mongodb.com/Node-REDhttps://nodered.org/Swarm Mode Overview | Docker Documentationhttps://docs.docker.com/engine/swarm/Kuberneteshttps://kubernetes.io/PIXEL Project D6.3 PIXEL Data Acquisition, Information Hub and Data Representation v1https://pixel-ports.eu/wp-content/uploads/2020/05/D6.3_PIXEL-data-acquisition-information-hub-and-data-representation-v1.pdfOverview of Docker Compose|Docker Documentationhttps://docs.docker.com/compose/Kibana: Explore, Visualize, Discover Datahttps://www.elastic.co/kibanaGrafana: The Open Observability Platformshttps://grafana.com/Vue.jshttps://vuejs.org/PIXEL Project D5.2 PEI Definition and Algorithms v1https://pixel-ports.eu/wp-content/uploads/2020/05/D5.2-PEI-Definition-and-Algorithms-v1.pdfKeyPerformanceIndicator—FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/KeyPerformanceIndicator/doc/spec/index.htmlWhat Is a Container?|App Containerization|Dockerhttps://www.docker.com/resources/what-containerGarcia-Alonso, L., Moura, T. G. Z., & Roibas, D. (2020). The effect of weather conditions on port technical efficiency. Marine Policy, 113, 103816. doi:10.1016/j.marpol.2020.103816TrafficThess—LIVE Traffic in Thessaloniki, Greecehttps://www.trafficthess.imet.gr/National Observatory of Athens—Meteo—Stations’ Live Data and Databasehttp://stratus.meteo.noa.gr/frontHow to Use Smart Data Models in Your Projects—FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/howto/index.htmlGan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., & Wu, J. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491-502. doi:10.1016/j.ecolind.2017.05.068Wilson, M. C., & Wu, J. (2016). The problems of weak sustainability and associated indicators. International Journal of Sustainable Development & World Ecology, 24(1), 44-51. doi:10.1080/13504509.2015.1136360Kumar, S. V., & Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 7(3). doi:10.1007/s12544-015-0170-8Prophet: Forecastig at Scalehttps://facebook.github.io/prophet/PIXEL Project D4.4 PredictiveAlgorithms v2https://pixel-ports.eu/wp-content/uploads/2020/05/PIXEL_D4.4_Predictive-Algorithms_v2.0_Final.pdfProject Jupyterhttps://jupyter.org/FIWARE Cygnushttps://fiware-cygnus.readthedocs.io/en/latest/NGSIElasticsearchSink—FIWARE Cygnushttps://fiware-cygnus.readthedocs.io/en/latest/cygnus-ngsi/flume_extensions_catalogue/ngsi_elasticsearch_sink/index.htmlNode.jshttps://nodejs.org/Elasticsearch Node.js Client [7.x]https://www.elastic.co/guide/en/elasticsearch/client/javascript-api/current/index.htmlApache HTTP Server Projecthttps://httpd.apache.org/Everything You Need to Know about Min-Max Normalization: A Python Tutorialhttps://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79OpenStreetMaphttps://www.openstreetmap.org/Leaflet—A JavaScript Library for Interactive Mapshttps://leafletjs.com/AmCharts: JavaScript Charts & Mapshttps://www.amcharts.com/FIWARE Cataloguehttps://www.fiware.org/developers/catalogue/Findlow, S. (2019). ‘Citizenship’ and ‘Democracy Education’: identity politics or enlightened political participation? British Journal of Sociology of Education, 40(7), 1004-1013. doi:10.1080/01425692.2019.1656910Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges. IEEE Communications Surveys & Tutorials, 20(1), 416-464. doi:10.1109/comst.2017.277115

AIoTES: Setting the principles for semantic interoperable and modern IoT-enabled reference architecture for Active and Healthy Ageing ecosystems

[EN] The average life expectancy of the world's population is increasing and the healthcare systems sooner than later will be compromised by its reduced capacity and its highly economic cost; in addition, the age distribution of the population is leading towards the older spectrum. This trend will lead to immeasurable and unexpected economic problems and social changes. In order to face up this challenge and complex economic and social problem, it is necessary to rely on the appropriate digital tools and technological infrastructures for ensuring that the elderly are properly cared in their everyday living environments and they can live independently for longer. This article presents ACTIVAGE IoT Ecosystem Suite (AIoTES), a concrete reference architecture and its implementation process that addresses these issues and that was designed within the first European Large Scale Pilot, ACTIVAGE, a H2020 funded project by the European Commission with the objective of creating sustainable ecosystems for Active and Healthy Ageing (AHA) based on Internet of Things and big data technologies. AIoTES offers platform level semantic interoperability, with security and privacy, as well as Big Data and Ecosystem tools. AIoTES enables and promotes the creation, exchange and adoption of crossplatform services and applications for AHA. The number of existing AHA services and solutions are quite large, especially when state-of-the-art technology is introduced, however a concrete architecture such as AIoTES gains more importance and relevance by providing a vision for establishing a complete ecosystem, that looks for supporting a larger variety of AHA services, rather than claiming to be a unique solution for all the AHA domain problems. AIoTES has been successfully validated by testing all of its components, individually, integrated, and in real-world environments with 4345 direct users. Each validation is contextualized in 11 Deployment Sites (DS) with 13 Validation Scenarios covering the heterogeneity of the AHA-IoT needs. These results also show a clear path for improvement, as well as the importance for standardization efforts in the ever-evolving AHA-IoT domain.We thank to all the people who have participated in the development and validation of AIoTES. This work has been developed under the framework of the ACTIVAGE project. The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 732679.Valero-López, CI.; Medrano-Gil, A.; González-Usach, R.; Julián-Seguí, M.; Fico, G.; Arredondo, MT.; Stavropoulos, TG.... (2021). AIoTES: Setting the principles for semantic interoperable and modern IoT-enabled reference architecture for Active and Healthy Ageing ecosystems. Computer Communications. 177:96-111. https://doi.org/10.1016/j.comcom.2021.06.0109611117

BigDaM: Efficient Big Data Management and Interoperability Middleware for Seaports as Critical Infrastructures

Over the last few years, the European Union (EU) has placed significant emphasis on the interoperability of critical infrastructures (CIs). One of the main CI transportation infrastructures are ports. The control systems managing such infrastructures are constantly evolving and handle diverse sets of people, data, and processes. Additionally, interdependencies among different infrastructures can lead to discrepancies in data models that propagate and intensify across interconnected systems. This article introduces “BigDaM”, a Big Data Management framework for critical infrastructures. It is a cutting-edge data model that adheres to the latest technological standards and aims to consolidate APIs and services within highly complex CI infrastructures. Our approach takes a bottom-up perspective, treating each service interconnection as an autonomous entity that must align with the proposed common vocabulary and data model. By injecting strict guidelines into the service/component development’s lifecycle, we explicitly promote interoperability among the services within critical infrastructure ecosystems. This approach facilitates the exchange and reuse of data from a shared repository among developers, small and medium-sized enterprises (SMEs), and large vendors. Business challenges have also been taken into account, in order to link the generated data assets of CIs with the business world. The complete framework has been tested in the main EU ports, part of the transportation sector of CIs. Performance evaluation and the aforementioned testing is also being analyzed, highlighting the capabilities of the proposed approach

The Port Environmental Index: A Quantitative IoT-Based Tool for Assessing the Environmental Performance of Ports

The increasing exchange of goods by sea is contributing significantly to pollution in port areas. Although several methods have been developed to assess the environmental performance of ports, most of them have shortcomings including a qualitative-only approach and self-assessment of environmental performance. Therefore, there is a pressing need to develop a different approach based on quantitative measurements obtained through measurements at ports. In this paper we present the Port Environmental Index (PEI), a quantitative composite index of port environmental performance driven by IoT. The index allows for environmental measurements to be collected in real time or close to real time through sensors providing an assessment of a port’s environmental performance in real time. In addition, since the methodology for creating the index is standardised, the index makes it possible to compare different ports and rank them in terms of their environmental performance. As a proof of concept (PoC) this paper also describes the application of the index to the port of Thessaloniki (Greece)