2021

• H. Moudoud and S. Cherkaoui, “Toward Secure and Private Federated Learning for IoT using Blockchain,” GLOBECOM 2022 – 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 4316-4321, doi: 10.1109/GLOBECOM48099.2022.10000623.
  [: arXiv ]

• A. Triwinarko, I. Dayoub, and S. Cherkaoui, “PHY layer enhancements for next generation V2X communication,” Vehicular Communications, vol. 32, Art. no. 100385, Dec. 2021, doi: 10.1016/j.vehcom.2021.100385.

• B. Nour, S. Cherkaoui and Z. Mlika, “Federated Learning and Proactive Computation Reuse at the Edge of Smart Homes,” in IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 3045-3056, 1 Sept.-Oct. 2022, doi: 10.1109/TNSE.2021.3131246.

• O. A. Dambri and S. Cherkaoui, “Modeling Self-Assembly of Polymer-Based Wired Nano-Communication Channel,” in IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 8, no. 2, pp. 107-118, June 2022, doi: 10.1109/TMBMC.2021.3118943.
  [: arXiv ]

• O. A. Dambri and S. Cherkaoui, “Modeling Self-Assembly of Polymer-Based Wired Nano-Communication Channel,” in IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, vol. 8, no. 2, pp. 107-118, June 2022, doi: 10.1109/TMBMC.2021.3118943. 

• A. Taïk, H. Moudoud and S. Cherkaoui, “Data-Quality Based Scheduling for Federated Edge Learning,” 2021 IEEE 46th Conference on Local Computer Networks (LCN), Edmonton, AB, Canada, 2021, pp. 17-23, doi: 10.1109/LCN52139.2021.9524974. [: arXiv ]

• A. Abouaomar, Z. Mlika, A. Filali, S. Cherkaoui and A. Kobbane, “A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks,” 2021 IEEE 46th Conference on Local Computer Networks (LCN), Edmonton, AB, Canada, 2021, pp. 273-280, doi: 10.1109/LCN52139.2021.9524882. [: arXiv ]

• Z. Mlika and S. Cherkaoui, “Network slicing for vehicular communications: A multi-agent deep reinforcement learning approach,” Annals of Telecommunications, vol. 76, pp. 665–683, 2021, doi: 10.1007/s12243-021-00872-w. [: arXiv ]

• A. Taik, Z. Mlika, S. Cherkaoui., “Data-Aware Device Scheduling for Federated Edge Learning,” IEEE Transactions on Cognitive Communications and Networking, 2021, doi:10.1109/TCCN.2021.3100574.
  [: arXiv ]

• A. Alalewi, I. Dayoub and S. Cherkaoui, “On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey,” in IEEE Access, vol. 9, pp. 107710-107737, 2021, doi: 10.1109/ACCESS.2021.3100472.
  [ : HAL UPHF]

• H. Moudoud, S. Cherkaoui and L. Khoukhi, “Towards a Scalable and Trustworthy Blockchain: IoT Use Case,” ICC 2021 – IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-6, doi: 10.1109/ICC42927.2021.9500535. [: arXiv ]

• Z. Mlika and S. Cherkaoui, “Competitive Algorithms and Reinforcement Learning for NOMA in IoT Networks,” ICC 2021 – IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-6, doi: 10.1109/ICC42927.2021.9500285.  [: arXiv ]

• E. Vagnoni, F. Gerini, R. Cherkaoui, and M. Paolone, “Digitalization in hydropower generation: Development and numerical validation of a model-based Smart Power Plant Supervisor,” IOP Conf. Ser.: Earth Environ. Sci., vol. 774, Art. no. 012107, 2021, doi: 10.1088/1755-1315/774/1/012107.

• O. Chughtai, M. H. Rehmani, L. Musavian, S. -M. Senouci, S. Cherkaoui and S. Mao, “IEEE Access Special Section Editorial: Flying Ad Hoc Networks: Challenges, Potentials, Future Applications, and Way Forward,” in IEEE Access, vol. 9, pp. 74189-74193, 2021, doi: 10.1109/ACCESS.2021.3077816.
  [ : MDPI ]

• H. Moudoud, S. Cherkaoui, and L. Khoukhi, “An overview of blockchain and 5G networks,” in Computational Intelligence in Recent Communication Networks (EAI/Springer Innovations in Communication and Computing), M. Ouaissa, Z. Boulouard, M. Ouaissa, and B. Guermah, Eds. Cham, Switzerland: Springer, 2022, doi: 10.1007/978-3-030-77185-0_1. [: arXiv ]

• Z. Mlika and S. Cherkaoui, “Massive IoT Access With NOMA in 5G Networks and Beyond Using Online Competitiveness and Learning,” in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13624-13639, 1 Sept.1, 2021, doi: 10.1109/JIOT.2021.3068061.
  [ : arXiv ]

• M. Lakoju, A. Javed, O. Rana, et al., “‘Chatty Devices’ and edge-based activity classification,” Discover Internet of Things, vol. 1, Art. no. 5, Feb. 2021, doi: 10.1007/s43926-021-00004-9. [ : Springer]

• A. Abouaomar, S. Cherkaoui, Z. Mlika and A. Kobbane, “Resource Provisioning in Edge Computing for Latency-Sensitive Applications,” in IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11088-11099, 15 July15, 2021, doi: 10.1109/JIOT.2021.3052082.
  [ :  arXiv ]

• Z. Mlika and S. Cherkaoui, “Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles,” in IEEE Network, vol. 35, no. 3, pp. 132-138, May/June 2021, doi: 10.1109/MNET.011.2000591. 
  [ :  arXiv ]

• M. Lakoju, A. Javed, O. Rana, P. Burnap, S. T. Atiba, and S. Cherkaoui, “‘Chatty Devices’ and edge-based activity classification,” Discover Internet of Things, vol. 1, Art. no. 5, Feb. 2021, doi: 10.1007/s43926-021-00004-9. [ :  cardiffmet.ac.uk pdf]

• Z. Mlika and S. Cherkaoui, “Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles,” in IEEE Network, vol. 35, no. 3, pp. 132-138, May/June 2021, doi: 10.1109/MNET.011.2000591.
  [🔓: arXiv ]

• A. Taik, B. Nour and S. Cherkaoui, “Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning,”  in IEEE Wireless Communications, vol. 28, no. 6, pp. 26-33, December 2021, doi: 10.1109/MWC.017.2100187.
  [🔓: arXiv ]