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30 results

Wattom: Ambient Eco-feedback with Mid-air Input

Conference Proceeding
Esteves, A., Quintal, F., Caires, F., Baptista, V., & Mendes, P. (2019)
Wattom: Ambient Eco-feedback with Mid-air Input. In 2019 5th Experiment International Conference (exp.at'19)https://doi.org/10.1109/EXPAT.2019.8876565
This paper presents Wattom, a highly interactive ambient eco-feedback smart plug that aims to promote a more sustainable use of electricity in the home. This paper describes o...

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...

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...

DIY Community WiFi Networks: Insights on Participatory Design

Conference Proceeding
Smyth, M., & Helgason, I. (2019)
DIY Community WiFi Networks: Insights on Participatory Design. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systemshttps://doi.org/10.1145/3290607.3313073
This paper presents a first version of a set of insights developed collaboratively by researchers during a three-year participatory design project spread across four European ...

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 ...

A Look at the Effects of Handheld and Projected Augmented-reality on a Collaborative Task

Conference Proceeding
Mackamul, E. B., & Esteves, A. (2018)
A Look at the Effects of Handheld and Projected Augmented-reality on a Collaborative Task. In SUI '18 Symposium on Spatial User Interactionhttps://doi.org/10.1145/3267782.3267793
This paper presents a comparative study between two popular AR systems during a collocated collaborative task. The goal of the study is to start a body of knowledge that descr...

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...