The second and final contractual review meeting for the BigDataOcean H2020 co-funded research project (http://www.bigdataocean.eu/site/) took place in Luxembourg on July 23, 2019. The BigDataOcean consortium successfully presented the results of the work of the project during the last 15 months of project (from M16 up until M30). UBITECH, as the leader of WP5 entitled BigDataOcean Web Platform Implementation, presented the updates in the technical architecture and the technical highlights of the BigDataOcean platform. UBITECH also supported ANEK and FOINIKAS in the presentation of the dedicated technical solutions that UBITECH implemented for them in the context of the project, namely the Fuel Consumption Reduction Investigation Application and the Fault Detection and Predictive Maintenance Application for ANEK and FOINIKAS respectively.
The second and final contractual review meeting for the AEGIS H2020 co-funded research project (https://www.aegis-bigdata.eu/) took place in Luxembourg on July 18, 2019. The AEGIS consortium successfully presented the results of the work of the project during the last 12 months of project (from M19 up until M30). UBITECH, as the leader of WP3 entitled System Requirements, User stories, Architecture and Micro-Services, presented the updates in the technical architecture and the final technical highlights of the AEGIS platform, as well as the enhanced functionalities of the Smart Home and Assisted Living demonstrator which was co-designed and developed with Suite5 and KONKAT.
The first contractual review meeting for the BigDataStack H2020 funded research project, under Grant Agreement No 779747, took place in Luxembourg on July 16, 2019. The BigDataStack project delivers an infrastructure management system for the holistic management of computing, storage and networking resources, encompassing techniques for runtime adaptations of all BigDataStack operations and realizing Data as a Service through seamless data functions across the complete data path and lifecycle. BigDataStack incorporates approaches that range from data-focused application analysis and dimensioning, process modelling, cluster resources / nodes characterization, management and runtime optimization, to information-driven networking.
The BigDataStack consortium successfully presented the results of the technical, coordination, dissemination and communication work performed during the first 18 months of the project. UBITECH, as the infrastructure provider, presented a live end-to-end demonstration of the integrated BigDataStack platform by coupling the infrastructure, the data services and the applications in a adaptable and seamless manner.
Following a peer-review process, Computer Methods and Programs in Biomedicine Journal published by Elsevier has accepted to publish a scientific manuscript entitled “Analyzing data and data sources towards a unified approach for ensuring end-to-end data and data sources quality in healthcare 4.0”, co-authored by UBITECH and UPRC. In this paper, Konstantinos Perakis, Dimitris Miltiadou, Stamatis Pitsios and their co-authors demonstrate an innovative mechanism for assessing the quality of various datasets in correlation with the quality of the corresponding data sources. For that purpose, the mechanism follows a 5-stepped approach through which the available data sources are detected, identified and connected to health platforms, where finally their data is gathered. Once the data is obtained, the mechanism cleans it and correlates it with the quality measurements that are captured from each different data source, in order to finally decide whether these data sources are being characterized as qualitative or not, and thus their data is kept for further analysis. https://doi.org/10.1016/j.cmpb.2019.06.026
A scientific paper entitled “Secure Edge Computing with Lightweight Control-Flow Property-based Attestation” has been co-authored by UBITECH and is presented at the 1st International Workshop on Cyber-Security Threats, Trust and Privacy Management in Software-defined and Virtualized Infrastructures (SecSoft), co-hosted at 5th IEEE International Conference on Network Softwarization (NetSoft 2019), between June 24-28, 2019 in Paris, France. In this paper, Sofianna Menesidou, Panagiotis Gouvas, and their co-authors propose a lightweight dynamic control-flow property-based attestation architecture (CFPA) that can be applied on both resource-constrained edge and cloud devices and services.
UBITECH is participating at the kick-off meeting, in Seville, Spain (June 4-5, 2019), of the SDN-microSENSE Innovation Action, officially started on May 1st, 2019. The project is funded by European Commission under Horizon 2020 Programme (Grant Agreement No. 833955) and spans on the period May 2019 – April 2022. The SDN-microSENSE project intends to provide a set of secure, privacy-enabled and resilient to cyberattacks tools, thus ensuring the normal operation of Electrical Power and Energy Systems (EPES) as well as the integrity and the confidentiality of communications.
In particular, adopting an SDN-based technology, SDN-microSENSE will develop a three-layer security architecture, by deploying and implementing risk assessment processes, self-healing capabilities, large-scale distributed detection and prevention mechanisms, as well as an overlay privacy protection framework. Firstly, the risk assessment framework will identify the risk level of each component of EPES, identifying the possible threats and vulnerabilities. Accordingly, in the context of self-healing, islanding schemes and energy management processes will be deployed, isolating the critical parts of the network in the case of emergency. Furthermore, collaborative intrusion detection tools will be capable of detecting and preventing possible threats and anomalies timely. Finally, the overlay privacy protection framework will focus on the privacy issues, including homomorphic encryption and anonymity processes.
A scientific paper entitled “Unveiling Trends and Predictions in Digital Factories” has been authored by UBITECH and is presented at the International Workshop on IoT Applications and Industry 4.0 (IoTI4 2019) that is part of the annual International Conference on Distributed Computing in Sensor Systems (DCOSS 2019), hosted between May 29-31, 2019 in Santorini, Greece. In this paper, Karagiorgou Sophia, Vafeiadis Georgios, Ntalaperas Dimitrios, Lykousas Nikolaos, Vergeti Danae and Alexandrou Dimitrios propose a failure prediction system for complex IT systems in the steel industry. The novelty of their work lies in the exploitation of Deep Learning techniques from streaming operational sensor data, enabling earlier failure predictions through a Neural Networks approach [in particular, through Long Short-Term Memory Networks (LSTM) that is a Recurrent Neural Network (RNN) architecture]. This predictive maintenance framework consists of three components: the Sense Module, the Detect Module and the Predict Module. To evaluate the proposed framework, real-life data are collected and analyzed based on daily operational and maintenance activities within the production line. They further demonstrate the framework’s potential by presenting some early results in modeling and predicting the complex and dynamic behavior in the manufacturing settings.
A scientific paper entitled “Personalised Monitoring and Recommendation Services for At-Risk Individuals Employing Machine-Learning and Decision Support” has been co-authored by UBITECH and is presented at the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), the flagship conference of IEEE Engineering in Medicine and Biology Society (IEEE-EMBS) on the topics of informatics and computing in healthcare and life sciences, hosted between May 19-22, 2019 in Chicago, IL, USA. In this paper, Perakis Konstantinos, Pitsios Stamatis, Miltiadou Dimitrios, and their co-authors propose a technological solution, facilitating the provision of personalised health related services exploiting Big Data analytics, aiming to improve the everyday living and enhance the wellbeing of vulnerable individuals such as chronic disease patients, focusing mainly on patients suffering from COPD and/or CVD.
Sponsoring the 3-day technology conference DockerCon 2019(https://www.docker.com/dockercon/) that takes place from April 29 – May 2 at Moscone West in San Francisco, UBITECH has a dedicated exhibition booth at the Ecosystem Expo of the conference, for presenting and demonstrating the sophisticated MAESTRO platform that enables distributed applications composition and cloud services orchestration. The MAESTRO platform (themaestro.net) is an advanced developer framework for cloud orchestration and infrastructure automation, that gives you the power to design, deploy, and manage cloud-native containerized components in both public and private cloud environments. Built with IaaS (Infrastructure-as-a-Service) abstraction, the MAESTRO platform lets you create easy-to-manage, easy-to-scale workflows with Docker Compose applications. It comes with advanced off-the-shelf features to support extensive monitoring, security enforcement, elasticity management, and operational analytics.
Following a peer-review process, Sensors MDPI Journal has accepted to publish a scientific manuscript, co-authored by UBITECH’s Konstantinos Perakis and Stamatis Pitsios, entitled “IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices”. Konstantinos Perakis, Stamatis Pitsios and their co-editors from UPRC present a mechanism for effectively implementing a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.