SWAMP:Smart Water Management Platform Overview and Security Challenges

The intensive use of technology in precision irrigation for agriculture is getting momentum in order to optimize the use of water, reduce the energy consumption and improve the quality of crops. Internet of Things (IoT) and other technologies are the natural choices for smart water management applications, and the SWAMP project is expected to prove the appropriateness of IoT in real settings with the deployment of on-site pilots. At the same time, the more intense the use of technology is, agriculture turns new security risks, which may affect both crop development and the commodities market. A security breach may irreversibly compromise a crop and data eavesdropping may compromise price and contracts exposing sensitive data such crop quality, development or management. This paper discusses security challenges and technologies for the application of IoT in agriculture and indicates that one of the most relevant challenges to be handled in SWAMP project is dealing with the multitude of behaviors from IoT application and what would be considered as normal and what would be considered as a threat

Mapping the NGSI-LD Context Model on Top of a SPARQL Event Processing Architecture: Implementation Guidelines

NGSI-LD is an open specification released by ETSI which proposes an information model and an API for an easy to use and standard management of context information. The NGSI-LD information model is framed within an ontology and adopts JSON-LD as serialization format for context information. This paper presents an approach to the implementation of the NGSI-LD specification over a SPARQL Event Processing Architecture. This work is being developed within the European-Brasilian H2020 SWAMP project focused on implementing an Internet of Things platform providing services for smart water management in agriculture

Implementation of end-user development success factors in mashup development environments

[EN] The Future Internet is expected to be composed of a mesh of interoperable web services accessed from all over the Web. This approach has been supported by many software providers who have provided a wide range of mash up tools for creating composite applications based on components prepared by the respective provider. These tools aim to achieve the end-user development (EUD) of rich internet applications (RIA); however, most, having failed to meet the needs of end users without programming knowledge, have been unsuccessful. Thus, many studies have investigated success factors in order to propose scales of success factor objectives and assess the adequacy of mashup tools for their purpose. After reviewing much of the available literature, this paper proposes a new success factor scale based on human factors, human-computer interaction (HCI) factors and the specialization-functionality relationship. It brings together all these factors, offering a general conception of EUD success factors. The proposed scale was applied in an empirical study on current EUD tools, which found that today's EUD tools have many shortcomings. In order to achieve an acceptable success rate among end users, we then designed a mashup tool architecture, called FAST-Wirecloud, which was built taking into account the proposed EUD success factor scale. The results of a new empirical study carried out using this tool have demonstrated that users are better able to successfully develop their composite applications and that FAST-Wirecloud has scored higher than all the other tools under study on all scales of measurement, and particularly on the scale proposed in this paper. (C) 2016 Elsevier B.V. All rights reserved.This research was partially supported by the European Union co-funded IST projects FAST: Fast and Advanced Storyboard Tools (GA 216048), FI-WARE: Future Internet Core Platform (GA 285248) and FI-CORE: Future Internet - Core (GA 632893). The FI-WARE and FI-CORE projects are part of the European Commission's Futuree Internet Public-Private Partnership (FI-PPP) initiative.Lizcano, D.; López, G.; Soriano, J.; Lloret, J. (2016). Implementation of end-user development success factors in mashup development environments. Computer Standards & Interfaces. 47:1-18. https://doi.org/10.1016/j.csi.2016.02.006S1184

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

Advancing IoT Platforms Interoperability

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

Advancing IoT Platforms Interoperability

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

DIN Spec 91345 RAMI 4.0 compliant data pipelining: An approach to support data understanding and data acquisition in smart manufacturing environments

Today, data scientists in the manufacturing domain are confronted with a set of challenges associated to data acquisition as well as data processing including the extraction of valuable in-formation to support both, the work of the manufacturing equipment as well as the manufacturing processes behind it. One essential aspect related to data acquisition is the pipelining, including various commu-nication standards, protocols and technologies to save and transfer heterogenous data. These circumstances make it hard to understand, find, access and extract data from the sources depend-ing on use cases and applications. In order to support this data pipelining process, this thesis proposes the use of the semantic model. The selected semantic model should be able to describe smart manufacturing assets them-selves as well as to access their data along their life-cycle. As a matter of fact, there are many research contributions in smart manufacturing, which already came out with reference architectures or standards for semantic-based meta data descrip-tion or asset classification. This research builds upon these outcomes and introduces a novel se-mantic model-based data pipelining approach using as a basis the Reference Architecture Model for Industry 4.0 (RAMI 4.0).Hoje em dia, os cientistas de dados no domínio da manufatura são confrontados com várias normas, protocolos e tecnologias de comunicação para gravar, processar e transferir vários tipos de dados. Estas circunstâncias tornam difícil compreender, encontrar, aceder e extrair dados necessários para aplicações dependentes de casos de utilização, desde os equipamentos aos respectivos processos de manufatura. Um aspecto essencial poderia ser um processo de canalisação de dados incluindo vários normas de comunicação, protocolos e tecnologias para gravar e transferir dados. Uma solução para suporte deste processo, proposto por esta tese, é a aplicação de um modelo semântico que descreva os próprios recursos de manufactura inteligente e o acesso aos seus dados ao longo do seu ciclo de vida. Muitas das contribuições de investigação em manufatura inteligente já produziram arquitecturas de referência como a RAMI 4.0 ou normas para a descrição semântica de meta dados ou classificação de recursos. Esta investigação baseia-se nestas fontes externas e introduz um novo modelo semântico baseado no Modelo de Arquitectura de Referência para Indústria 4.0 (RAMI 4.0), em conformidade com a abordagem de canalisação de dados no domínio da produção inteligente como caso exemplar de utilização para permitir uma fácil exploração, compreensão, descoberta, selecção e extracção de dados