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.
UBITECH participates in the kick-off meeting, in Limassol, Cyprus of the PERSEPHONE Cypriot research project, officially started on May 21st, 2019. The project is co-funded by the “Research in Enterprises” action of the multi-annual development framework of Programmes RESTART 2016-2020 for the support of Research, Technological Development and Innovation in Cyprus (Grant Agreement No. ENTERPRISES/0916/0063) and spans on the period May 2019 – May 2021. The PERSEPHONE project aims to design and deploy an innovative IT ecosystem for motivating end-users’ behavioural changes towards the adoption of energy efficient lifestyles, building upon the evolvements in the Internet of Things, Data Modelling and Analysis and Recommendation and Gamification eras.
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(
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.
UBITECH proudly announces the delivery of the final and significantly enhanced version of the 
UBITECH proudly supports (for the third year in a row) the 
A scientific paper entitled “Anonymizing Clinical and Genetic Data of Patients with Minimum Information Loss” has been authored by UBITECH and is presented at the 5th Collaborative European Research Conference (CERC 2019), hosted between March 29-30, 2019 in Darmstadt, Germany. In this paper, Dimitris Ntalaperas and Thanassis Bouras propose an approach for data anonymization to foster data exchange, which is based on disclosing a row based anonymized version of the original data set. The methodology is more versatile (than traditional data cubes -oriented approaches), while it also preserves the statistical characteristics of the original data set. We demonstrate this by considering an SVM predictor that tries to estimate the value of Breslow’s depth, based on the values of another clinical variable, namely Clark’s level, and the expression count of a skin cancer related gene (CDKN2A). The predictions are shown to have the same characteristics for both the original and the anonymized data sets.
