Diabetes research: at Edinburgh Napier University's School of Computing
Digital Artefact
Ryan, B., & Webster, G. (2020)
Diabetes research: at Edinburgh Napier University's School of Computing. [Blog]
This 'Diabetes Research' blog is initially about the seed-project 'Information Avoidance and diabetes'. This project is an initial investigation of how and why people with dia...
An AI approach to Collecting and Analyzing Human Interactions with Urban Environments
Journal Article
Ferrara, E., Fragale, L., Fortino, G., Song, W., Perra, C., di Mauro, M., & Liotta, A. (2019)
An AI approach to Collecting and Analyzing Human Interactions with Urban Environments. IEEE Access, 7, 141476-141486. https://doi.org/10.1109/access.2019.2943845
Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, i...
Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach
Journal Article
Di Mauro, M., & Liotta, A. (2019)
Statistical Assessment of IP Multimedia Subsystem in a Softwarized Environment: a Queueing Networks Approach. IEEE Transactions on Network and Service Management, 16(4), 1493-1506. https://doi.org/10.1109/tnsm.2019.2943776
The Next Generation 5G Networks can greatly benefit from the synergy between virtualization paradigms, such as the Network Function Virtualization (NFV), and service provision...
The Social Impact of Digital Youth Work: What Are We Looking For?
Journal Article
Pawluczuk, A., Webster, G., Smith, C., & Hall, H. (2019)
The Social Impact of Digital Youth Work: What Are We Looking For?. Media and Communication, 7(2), 59-68. https://doi.org/10.17645/mac.v7i2.1907
Digital youth work is an emerging field of research and practice which seeks to investigate and support youth-centred digital literacy initiatives. Whilst digital youth work p...
Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks
Journal Article
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,...
Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities
Journal Article
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 ...
Interference graphs to monitor and control schedules in low-power WPAN
Journal Article
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
Highlights
• This study presents the complete and slotted interference graph model.
• The service uses the complete interference graph to evaluate the network.
• Slotted int...
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Journal Article
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 ...
An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0
Journal Article
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...
On-Line Building Energy Optimization Using Deep Reinforcement Learning
Journal Article
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 ...