Professor of Data Science and Intelligent Systems
Lecturer in Cybersecurity
Sébastien Bedo, J., Eddine El Ayoubi, S., Filippou, M., Gavras, A., Giustiniano, D., Iovanna, P., …Tjelta, T. (2017). 5G innovations for new business opportunities. Brussels: European Commission / 5G PPP
5G is the next generation mobile network that enables
innovation and supports progressive change across all
vertical industries and across our society1. Through its
Khan, R., Zakarya, M., Tan, Z., Usman, M., Jan, M. A., & Khan, M. (in press). PFARS: Enhancing Throughput and Lifetime of Heterogeneous WSNs through Power-aware Fusion, Aggregation and Routing Scheme. International Journal of Communication Systems,
Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, ...
Mclaren, P., Russell, G., Buchanan, W. J., & Tan, Z. (2019). Decrypting Live SSH Traffic in Virtual Environments. Digital Investigation, 29, 109-117. https://doi.org/10.1016/j.diin.2019.03.010
Decrypting and inspecting encrypted malicious communications may assist crime detection and prevention. Access to client or server memory enables the discovery of artefacts re...
Arts & Humanities Research Council
The feeling of being immersed within a live performance by an ensemble of world class musicians can be a deeply engaging and highly valued cultural experience for audiences as well as for the performe...
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371
High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning,...
Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., …Liotta, A. (2019). Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities. IEEE Access, 7, 19890-19906. https://doi.org/10.1109/access.2019.2897217
The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data ...
van der Lee, T., Liotta, A., & Exarchakos, G. (2019). Interference graphs to monitor and control schedules in low-power WPAN. Future Generation Computer Systems, 93, 111-120. https://doi.org/10.1016/j.future.2018.10.014
• This study presents the complete and slotted interference graph model.
• The service uses the complete interference graph to evaluate the network.
• Slotted int...
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018). Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-04316-3
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from ...
Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2019). An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 481-489. https://doi.org/10.1109/tii.2018.2843169
Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and t...
Lenka, R. K., Rath, A. K., Tan, Z., Sharma, S., Puthal, D., Simha, N. V. R., …Tripathi, S. S. (2018). Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors. IEEE Access, 6, 30162-30173. https://doi.org/10.1109/ACCESS.2018.2842760
Wireless Sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to ...
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019). On-Line Building Energy Optimization Using Deep Reinforcement Learning. IEEE Transactions on Smart Grid, 10(4), 3698-3708. https://doi.org/10.1109/tsg.2018.2834219
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the ...
Torres Vega, M., Perra, C., De Turck, F., & Liotta, A. (2018). A Review of Predictive Quality of Experience Management in Video Streaming Services. IEEE Transactions on Broadcasting, 64(2), 432-445. https://doi.org/10.1109/tbc.2018.2822869
Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but als...
Chincoli, M., & Liotta, A. (2018). Self-Learning Power Control in Wireless Sensor Networks. Sensors, 18(2), 1-29. https://doi.org/10.3390/s18020375
Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This...
Mocanu, D. C., Exarchakos, G., & Liotta, A. (2018). Decentralized dynamic understanding of hidden relations in complex networks. Scientific Reports, 8(1), https://doi.org/10.1038/s41598-018-19356-4
Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in s...
Torres Vega, M., Perra, C., & Liotta, A. (2018). Resilience of Video Streaming Services to Network Impairments. IEEE Transactions on Broadcasting, 64(2), 220-234. https://doi.org/10.1109/tbc.2017.2781125
When dealing with networks, performance management through conventional quality of service (QoS)-based methods becomes difficult and is often ineffective. In fact, quality eme...
Torres Vega, M., Koonen, A. M. J., Liotta, A., & Famaey, J. (2018). Fast Millimeter Wave Assisted Beam-Steering for Passive Indoor Optical Wireless Networks. IEEE Wireless Communications Letters, 7(2), 278-281. https://doi.org/10.1109/lwc.2017.2771771
In light of the extreme radio congestion, the time has come to consider the upper parts of the electromagnetic spectrum. Optical beam-steered wireless communications offer gre...
Swarm robotics is a special case within the general field of robotics. The distributed nature makes it...