Performance of Cognitive Radio Sensor Networks Using Hybrid Automatic Repeat ReQuest: Stop-and-Wait
Khan, F., ur Rehman, A., Usman, M., Tan, Z., & Puthal, D. (2018)
Performance of Cognitive Radio Sensor Networks Using Hybrid Automatic Repeat ReQuest: Stop-and-Wait. Mobile Networks and Applications, https://doi.org/10.1007/s11036-018-1020-4
The enormous developments in the field of wireless communication technologies have made the unlicensed spectrum bands crowded, resulting uncontrolled interference to the tradi...
Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines
Mocanu, D. C., Bou Ammar, H., Puig, L., Eaton, E., & Liotta, A. (2017)
Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. Pattern Recognition, 69, 325-335. https://doi.org/10.1016/j.patcog.2017.04.017
Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficul...
Interference graphs to monitor and control schedules in low-power WPAN
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...
A topological insight into restricted Boltzmann machines
Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M., & Liotta, A. (2016)
A topological insight into restricted Boltzmann machines. Machine Learning, 104(2-3), 243-270. https://doi.org/10.1007/s10994-016-5570-z
Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic feature...
On-Line Building Energy Optimization Using Deep Reinforcement Learning
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 ...
Spatial anomaly detection in sensor networks using neighborhood information
Bosman, H. H., Iacca, G., Tejada, A., Wörtche, H. J., & Liotta, A. (2017)
Spatial anomaly detection in sensor networks using neighborhood information. Information Fusion, 33, 41-56. https://doi.org/10.1016/j.inffus.2016.04.007
The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capability, has now matured after a decade-long research effort and technological ad...
A Review of Predictive Quality of Experience Management in Video Streaming Services
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...
Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities
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 ...
Self-Learning Power Control in Wireless Sensor Networks
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...
A Chain Topology for Efficient Monitoring of Food Grain Storage using Smart Sensors
Kumar Mishra, A., Kumar Tripathy, A., Obaidat, M. S., Tan, Z., Prasad, M., Sadoun, B., & Puthal, D. (2018)
A Chain Topology for Efficient Monitoring of Food Grain Storage using Smart Sensors. In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications, (89-98). https://doi.org/10.5220/0006850602550264
Due to lack of an efficient monitoring system to periodically record environmental parameters for food grain storage, a huge loss of food grains in storage is reported every y...