Vasos Vassiliou


Associate Professor, Department of Computer Science, University of Cyprus
Founding Academic Member and Research Group Leader, Smart Networked Systems (SNS) Multidisciplinary Research Group, CYENS Research Center

Department of Computer Science
University of Cyprus
1 University Avenue
2109 Nicosia,
Office: FST01 (ΘΕΕ01) B114
vasosv--at-- {} or {}
+ 357-22-892750

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My research is mainly systems and protocols oriented. I design and deploy network protocols and systems that make the Internet work better. Most of my work is used in a representative environment (test-bed or simulation) and tested with real-life values, parameters and settings. I use empirical network measurement and machine learning to understand and improve network performance, reliability, and security.

Social and Context-aware Content Distribution in 5G Networks

Social and Context-aware Content Distribution in 5G Networks (current): Exploit 5G dense networks and trends in using the network edge for processing and storage. Develop and integrate a framework for QoE-based dynamic adaptation of network and content. This includes the fusion of concepts from social network cascades and content dissemination, the increasing use of small high-speed cells for network communication, and the ability to group and predict users’ needs.

Intelligent approaches in D2D solutions for 5G

As D2D communications become more prevalent in 5G networks, both with the use of in-bad and out-band communication modes, we are considering the use of AI-based techniques to address the major challenges of Interference management, Cell densification and offloading, QoS/Path Selection (Routing), Handovers of D2D devices, Device Discovery and Power management. The bet is essentially to create a solution that is truly distributed and dynamic. After investigating all the D2D requirements and available solutions, we believe that using BDI (Belief, Desire, intention) agents can help at the implementation of D2D as a distributed, dynamic and autonomous control system.

Intrusion Detection in IoT Networks

protocols and algorithms for the secure and reliable operation of IoT networks. Significant opportunities exist in clearly defining the scope of security solutions and IoT-specific topologies considered. Extensions to the BLR-based anomaly detection will be defined, to cover different attacks, different topologies and different agent locations. New IDS techniques based on Computational Intelligence (Fuzzy logic, Artificial Immune System) and Machine Learning (SVM, K-NN, Q-learning) will be developed.

Fault recovery in WSNs and IoT using Mobile Nodes

The introduction of robotics and UAVs in WSNs and IoT networks makes the work on mobile nodes relevant and timely. Cross-layer techniques for fault identification (energy depletion, congestion, hardware failure, malicious operation) will be developed. These events will trigger different solutions of mobile node utilization for recovery.

Selected Publications (full list)

Current PhD Students