| Using correlated information to extend device lifetime J Hribar, M Costa, N Kaminski, LA DaSilva IEEE Internet of Things Journal 6 (2), 2439-2448, 2018 | 40 | 2018 |
| Using deep Q-learning to prolong the lifetime of correlated internet of things devices J Hribar, A Marinescu, GA Ropokis, LA DaSilva 2019 IEEE International Conference on Communications Workshops (ICC …, 2019 | 18 | 2019 |
| Utilising correlated information to improve the sustainability of internet of things devices J Hribar, L DaSilva 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 805-808, 2019 | 18 | 2019 |
| Updating strategies in the Internet of Things by taking advantage of correlated sources J Hribar, M Costa, N Kaminski, LA DaSilva GLOBECOM 2017-2017 IEEE Global Communications Conference, 1-6, 2017 | 16 | 2017 |
| Resource reservation within sliced 5g networks: A cost-reduction strategy for service providers JB Monteil, J Hribar, P Barnard, Y Li, LA DaSilva 2020 IEEE International Conference on Communications Workshops (ICC …, 2020 | 12 | 2020 |
| Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space J Hribar, A Marinescu, A Chiumento, LA Da Silva IEEE Internet of Things Journal, 2021 | 11 | 2021 |
| Federated spatial reuse optimization in next-generation decentralized IEEE 802.11 WLANs F Wilhelmi, J Hribar, SF Yilmaz, E Ozfatura, K Ozfatura, O Yildiz, ... arXiv preprint arXiv:2203.10472, 2022 | 5 | 2022 |
| Timely and sustainable: Utilising correlation in status updates of battery-powered and energy-harvesting sensors using deep reinforcement learning J Hribar, LA DaSilva, S Zhou, Z Jiang, I Dusparic Computer Communications 192, 223-233, 2022 | 2 | 2022 |
| Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning J Hribar, R Shinkuma, G Iosifidis, I Dusparic 2021 IEEE Global Communications Conference (GLOBECOM), 2021 | 2 | 2021 |
| Machine Learning Operations Model Store: Optimizing Model Selection for AI as a Service G Cerar, J Hribar | 1 | 2023 |
| Enabling Deep Reinforcement Learning on Energy Constrained Devices at the Edge of the Network J Hribar, I Dusparic 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022 | 1 | 2022 |
| Method and system for energy aware scheduling for sensors L Dasilva, J Hribar US Patent App. 17/129,284, 2021 | 1 | 2021 |
| SMART: Situationally-aware multi-agent reinforcement learning-based transmissions Z Jiang, Y Liu, J Hribar, LA DaSilva, S Zhou, Z Niu IEEE Transactions on Cognitive Communications and Networking 7 (4), 1430-1443, 2021 | 1 | 2021 |
| A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends HNC Neto, J Hribar, I Dusparic, DMF Mattos, NCC Fernandes IEEE ACCESS 11, 41928-41953, 2023 | | 2023 |
| Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning J Hribar, L Hackett, I Dusparic arXiv preprint arXiv:2211.04813, 2022 | | 2022 |
| Taking advantage of correlated information for energy-aware scheduling in the IoT: A deep reinforcement learning approach J Hribar Trinity College Dublin. School of Engineering. Discipline of Electronic …, 2020 | | 2020 |
Luiz DaSilvaVirginia Tech在 vt.edu 的电子邮件经过验证
