Fellows

Georgios Argyriou

  • Position: RAIS-UCY-ESR1
  • Title: Adaptive Monitoring Framework for Wearable Devices
  • Host Institution: University of Cyprus (UCY), Cyprus
  • Supervisory Committee:
    Prof. Marios Dikaiakos / Dr. George Pallis
  • Start Date: 2019-09-02
  • Keywords: Middleware on Edge, Serverless programming on Edge, Adaptive monitoring for IoT
  • Research:

    The main objective of this research is to build a monitoring framework for wearable devices that takes into account their key characteristics: limited processing power, heterogeneity, dissemination of sensitive data, low-energy sufficiency (battery-powered), intermittent network connectivity. State-of-the-art research work will be taken into account with emphasis to new platforms and tools that involve IoT devices and Fog/Edge computing nodes. The serverless programming model is a promising solution towards that direction. Extensions to a pre-existing platform that enrich its functionality can end up in a middleware prototype that can be deployed on Edge/Fog nodes. For battery powered edge devices we focus on lowering power consumption. Towards that end, we can propose techniques for lowering the amount of data dissemination or find smart ways to adapt computation execution to the appropriate resources.

Ioannis Savvidis

  • Position: RAIS-UCY-ESR2
  • Title: Blockchain-based Middleware for Distributed Sensor Environments
  • Host Institution: University of Cyprus (UCY), Cyprus
  • Supervisory Committee:
    Prof. Marios Dikaiakos / Dr. George Pallis
  • Start Date: 2019-11-15
  • Keywords: Blockchain for IoS, Consensus Algorithms, Data economy
  • Research:

    Currently, we are working on a project that will enable data monetization. The project has two aspects. On the one hand, the goal is to develop a framework which allows the data providers to create value from their data. On the other hand, it allows data consumers to use the data for operations such as model training or aggregation. For this purpose, we make use of blockchain technology and cryptographic techniques. The blockchain can inherently provide immutability, security and availability while cryptography will provide a greater level of privacy. Also, the framework will be focused on the fair sharing of the profit among the data providers. The contribution of each provider is not strictly related to the volume of the data but its quality too. On this context, we explore the use of models derived from the game theory such as Shapley value, for the calculation of the contribution of each provider. Thus, the recompense of each provider will be proportional to her contribution to the final result.

Lodovico Giaretta

  • Position: RAIS-KTH-ESR3
  • Title: Decentralized Data Mining & Information Network Analytics
  • Host Institution: Royal Institute of Technology (KTH), Sweden
  • Supervisory Committee:
    Prof. Šarūnas Girdzijauskas
  • Start Date: 2019-07-01
  • Keywords: Graph Representation Learning, Decentralized Machine Learning, Machine Learning over Networks
  • Research:

    The world we live in can be often described in terms of networks: social networks, computer networks, networks of Internet of Things or Internet of Sports devices. Understanding the phenomena underlying these networks is fundamental in order to advance our society. My work within the RAIS project focuses on two key issues related to this topic.
    The first issue is how to use Machine Learning to understand the structure of complex networks and predict their evolution. The second issue is how to safely and effectively perform Machine Learning tasks on top of an existing network of devices, in a decentralized fashion, exploiting the computational power of all these devices, while still maintaining their data private. The final goal of my research is to be able to combine the solutions of these two issues, known as Graph Representation Learning and Decentralized Privacy-Preserving Machine Learning, in a way that can be directly applied to a variety of tasks.
    As an example application, consider an Internet of Sports sensor/app that can record the user activity and his/her interactions with other users. With these technologies, it would be possible for the app to analyze the behaviour of the user and provide exercise recommendations, without sharing any raw information with any central entity, thus providing the best user experience without compromising privacy.

Ha Xuan Son

  • Position: RAIS-INSUB-ESR4
  • Title: User-Centric Personal Data Management
  • Host Institution: University of Insubria (INSUB), Italy
  • Supervisory Committee:
    Prof. Elena Ferrari and Prof. Barbara Carminati
  • Start Date: 2019-10-01
  • Keywords: Privacy Risk models for IoT apps, Privacy preserving personal data storage for IoT/IoS, Edge to cloud Privacy driven data transfer
  • Research:

    The project I am working on aims at designing and developing a model and a system to protect user personal data in IoT ecosystems by moving from a service-centric way of securing personal data (which is today the de facto standard) to a user-centric approach. This will empower individuals with tools to manage their privacy preferences over data produced through different IoT/wearable devices. Adopting a user-centric model will allow us to design a system that offers individuals more fine-grained control on how their data are released, we plan to achieve an information-sharing approach driven also by trust relationships with data consumers. The system will be complemented with tools leveraging on data mining and machine learning techniques able to: automatically adjust user privacy settings when new devices enter the personal pervasive ecosystem and adapt privacy settings to change with minimal user intervention.

Andrei Kazlouski

  • Position: RAIS-FORTH-ESR5
  • Title: Mitigation of Cyber Attacks in wearable Devices
  • Host Institution: Foundation for Research and Technology - Hellas (FORTH), Greece
  • Supervisory Committee:
    Prof. Evangelos Markatos
  • Start Date: 2019-11-01
  • Keywords: Attacks on Wearable Sensors, Privacy of Wearable Devices data, Deanonymization of Wearable Devices data
  • Research:

    Here at FORTH within RAIS project we are trying to discover and address security and privacy limitations of centralized handling for sensitive IoT/IoS health data. In particular, we are investigating ways to disclose sensitive personal information by intercepting and analyzing data (potentially encrypted) sent by wearable devices to the cloud. To do that we are employing cryptoanalysis and various machine learning techniques. Cryptanalytic approaches include comparing pairs of plain text and encrypted data, as well as analyzing specific bits of encrypted payload. Machine learning approaches are based on identifying relevant features of encrypted data: DNS, IP, ports, time intervals, etc. Furthermore, we are studying existing cybersecurity attacks on wearable devices, and ways to mitigate them. We are replicating these attacks, and designing pipelines to neutralize them. Finally, we are trying to scrutinize impact of General Data Protection Regulation (GDPR) on IoS. We are surveying whether major wearable devices manufacturers are complying with GDPR.

Thomas Marchioro

  • Position: RAIS-FORTH-ESR6
  • Title: Decentralized privacy-preserving data sharing
  • Host Institution: Foundation for Research and Technology - Hellas (FORTH), Greece
  • Supervisory Committee:
    Prof. Evangelos Markatos
  • Start Date: 2019-11-01
  • Keywords: Security of Wearable Devices, Anonymization techniques for Blockchain, Trust-based consensus protocols, Privacy of IoT healthcare data
  • Research:

    The emergence of new data-driven technologies for the Internet of Sports creates new ways of helping people in their everyday activities, but on the other hand it also raises privacy concerns with regard to their sensitive data. Nowadays, most of the IoS data are sent from users’ devices towards proprietary clouds and databases, where users lose control over them. The main topic of my research at FORTH, within the RAIS project, consists in assessing the privacy risks related the use of these centralized systems in the Internet of Sports domain, with a focus on wearable devices, and evaluating the possible countermeasures. Moreover, I study decentralized alternatives that reduce such risks without impacting on the efficiency of IoS services.

Ahmed Lekssays

  • Position: RAIS-INSUB-ESR7
  • Title: Blockchain-Based Distributed Secure Data Management
  • Host Institution: University of Insubria (INSUB), Italy
  • Supervisory Committee:
    Prof. Elena Ferrari and Prof. Barbara Carminati
  • Start Date: 2019-10-01
  • Keywords: Malware Detection in IoT/IoS, Privacy in Blockchain, Blockchain for IoT/IoS, Cyber Attacks on IoT/IoS
  • Research:

    At the University of Insubria, we are currently working on security and privacy for IoT/IoS devices. We explore privacy-preservation techniques for processes involving data sharing in IoT environment. Besides, we are working on decentralized malware detection in IoT networks by analyzing network flows. For this matter, we adopt blockchain as a distributed solution to ensure transparency and immutability of malware detection results in an IoT setting. Our research takes into consideration the exclusive aspects of IoT, such as low computational power, in order to develop efficient algorithms to ensure the operability of the IoT devices while protecting them.

Susanna Pozzoli

  • Position: RAIS-KTH-ESR8
  • Title: Decentralized Data Mining & Information Network Analytics
  • Host Institution: Royal Institute of Technology (KTH), Sweden
  • Supervisory Committee:
    Prof. Šarūnas Girdzijauskas
  • Start Date: 2019-06-25
  • Keywords: Role Discovery, Analysis of Higher-Order Networks, Machine Learning Networks, Distributed Systems
  • Research:

    Networks are popular data structures that embed relationships between entities in edges between nodes. Although they have many applications, including, but not limited to, Internet of Sports, it is hard to extract insights into the roles of the nodes. Roles are able to capture the higher-order connectivity patterns of the nodes and thus to emphasize the behaviors of the entities.
    Within RAIS, I am focusing on role discovery, which has many applications ranging from data anonymization to online anomaly detection. Particularly, I will research into possible ways of carrying out an analysis of roles in networks in a distributed manner.
    Currently, I am reviewing the topic of role discovery in order that strengths and weaknesses in current methods may be recognized.

Debaditya Roy

  • Position: RAIS-WMOVE-ESR9
  • Title: Distributed Machine Learning on Data Streams
  • Host Institution: Royal Institute of Technology (KTH), Sweden
  • Supervisory Committee:
    Prof. Christer Norström / Dr. Mohammed El-Beltagy / Prof. Šarūnas Girdzijauskas
  • Start Date: 2019-10-14
  • Keywords: Pose estimation/Motion modelling using deep learning, Real time feedback on poses, Reinforcement learning, Distributed Machine learning
  • Research:

    My present research work focusses on motion modelling using artificial intellgience. Presently I am exploring the domain of human pose estimation in a minimalistic approach. Primarily, we are focussing on estimating human pose only from one IMU sensor providing accelerometer readings. This approach would enable a low cost pose estimation technique that can easily be integrated with any IOT ecosystem revolving around accelerometer readings (e.g. mobile phone) and will greatly contribute towards personalized coaching feedback for athletes (for whom a good pose equivalents to good technique. Furthermore, it can be extended to multiple other use cases of health and well being. We will build a system leveraging machine learning techniques that would take in accelerometer signals from running users of Racefox and estimate poses for those users in a fast and effective manner. Furthermore, we plan to extend our research to explore the possibility of training our deep learning/machine learning models over a distributed infrastructure and experiment with different edge settings for deployment of our model.  Additionally, we also plan to explore the scope of reinforcement learning for personalization and recommendation.

Ahmed Emad Samy Yossef Ahmed

  • Position: RAIS-KTH-ESR10
  • Title: Decentralized Data Mining & Information Network Analytics
  • Host Institution: Royal Institute of Technology (KTH), Sweden
  • Supervisory Committee:
    Prof. Šarūnas Girdzijauskas
  • Start Date: 2019-11-25
  • Keywords: Context-aware Learning, Graph Representation Learning, Decentralized and Distributed Machine Learning, IoT based Learning
  • Research:

    My current research focuses on designing distributed/decentralized algorithms for personalized machine Learning for IoT, where only access to partial information is assumed. Supervised and unsupervised ML algorithms for decentralized setting are being investigated. Gossip learning will be compared to federated learning under different proposed conditions such as homogeneity and security vs trustiness in the network. Furthermore, we plan to extend our research to explore decentralized context-aware learning will be explored. Leveraging information from the surrounding context can be very useful. For this part of the project, a key assumption and motivation is that incorporating contextual information can improve the results of the model globally for the network, and more personally for the nodes. Towards that end, different applications and tasks are intended to be explored such as personalized recommendation and graph representation learning in decentralized fashion. Among the machine learning algorithms, the graph neural networks are to be explored, as well.   

John Abied Hatem

  • Position: RAIS-UCY-ESR11
  • Title: Exercise Habits and Social Contagion Patterns
  • Host Institution: University of Cyprus (UCY), Cyprus
  • Supervisory Committee:
    Dr. Christos Nicolaides / Dr. George Pallis
  • Start Date: 2019-11-05
  • Keywords: Habits, Information Theoretic Measures, Causal Inference
  • Research:

    My current research activity within RAIS can be summarized by this intriguing question: How can we measure something as abstract as exercise habits from observational datasets? Well, the first step will be building a solid understanding of exercise habits in particular and habits in general. Based on this understanding, an in-depth investigation of a large observational dataset will be performed to identify behavioral attributes that have habitual nature in such datasets. Next, will be developing measures capable of quantifying habitual behavior from real observational datasets.  Finally, these measures will be used to predict long-term exercise behavior and future retention of habits. In short, my work is a fascinating intertwining of the disciplines of psychology, information theory, data science, and machine learning. It will provide measures to quantify habits from increasingly available observational exercise data and will offer insights into how habits shape our behaviors.

Vangjush Komini

  • Position: RAIS-WMOVE-ESR12
  • Title: Real Time Anomaly Detection
  • Host Institution: Royal Institute of Technology (KTH), Sweden
  • Supervisory Committee:
    Prof. Šarūnas Girdzijauskas/Prof. Christer Norström / Dr. Mohammed El-Beltagy
  • Start Date: 2019-08-19
  • Keywords: Feature Selection, Anomaly Detection Classification, Personalization, Uncertainity Quantification, Machine Learning
  • Research:

    Machine learning is undoubtedly a revolutionary technology in closing the gap between personalized services and health care. At the moment, the research activity undertaken at the RAIS program is developing advanced (deep) machine learning systems capable of early prediction of potential deterioration of leg (knee) status.  This continuous risk prediction of upcoming injuries, especially in athletes who have undergone in the past some knee surgery, could mitigate any re-occurrence of adverse events at the same time comforts the recovery period.
    The current roadmap for this issue contains a combination of recurrent neural networks and model-based reinforcement learning. A follow-up within the scope of injury prevention is also a personalized guidance system able to provide continuous recommendations for potentially risky situations.

Stefanos Efstathiou

  • Position: RAIS-AUTH-ESR13
  • Title: Framework for IoS Performance Analytics
  • Host Institution: Aristotle University of Thessaloniki (AUTH), Greece
  • Supervisory Committee:
    Prof. Athena Vakali
  • Start Date: 2019-10-09
  • Keywords: Human Activity Recognition, Behavioral Context Recognition, Multi-modal sensing, Sensing in the wild, Machine Learning
  • Research:

    The research activity I am currently focusing within RAIS is smartphone and wearable devices sensing applied in user experiments designed 'in the wild'.  'In the wild' is an approach which recent years gained popularity due to better understanding on how mobiles and wearable devices can be used  in the everyday/real world, in order to gain new insights regarding the use of smartphone and wearable devices for tracking peoples health and wellbeing - for example engagements, emotions, interactions, activities etc. To support this approach, I am conducting a survey to examine current sensing methodologies, data collection methods, machine learning algorithms, applications that are currently being used in these user experiments and to spot gaps, limitations and explore future research directions and next steps for smartphone and wearable devices sensing.

Sofia Yfantidou

  • Position: RAIS-AUTH-ESR14
  • Title: Human Behavioral Patterns for Sustained Engagement
  • Host Institution: Aristotle University of Thessaloniki (AUTH)
  • Supervisory Committee:
    Prof Athena Vakali
  • Start Date: 2019-12-02
  • Keywords: User Engagement, Persuasive Technology, Personal Informatics, Human-Computer Interaction, Ubiquitous Computing
  • Research:

    My research activity within the RAIS project is focused on the analysis of behavioral patterns to achieve sustained user engagement. Wearable devices and technological solutions for physical well-being suffer from high attrition rates, while having immense unexplored potential in terms of personalization at the same time. To unveil the unexploited possibilities of wearable devices, I am currently conducting an in-depth, interdisciplinary literature review, to identify current research gaps in the field of healthy habit formation and user engagement. Hence, my work lies in the intersection between Computer Science, Behavioral Sciences, and Sports Science. My goal is to utilize technology as a driver of change, rather than a secondary tool in the Internet of Sports (IoT) domain. To this end, I am planning to exploit state-of-the-art, privacy-preserving Machine Learning and analytics, to explore the habits, behaviors, motivations and barriers of people when it comes to physical well-being, and help them run more active lives in the long-term through the use of wearable technology.