This project is in the field of information technology and aims to develop Internet of Things solutions to address a number of challenges faced by African countries, such as drought and traffic jam in urban areas. This research team will contribute to the application of the most recent techniques in Africa, such as smart farming and self-driving vehicles.
Keywords: IoT, 5G, Machine Learning
El-Mehdi Amhoud
El-Mehdi is currently an Assistant Professor at Mohammed VI Polytechnic University, Morocco. His research interests include the energy efficiency and reliability of Internet of Things (IoT) networks and future generations of wireless communications.
Andreas Burg
In 2011, he became a Tenure Track Assistant Professor at EPFL where he is leading the Telecommunications Circuits Laboratory in the School of Engineering. Since 2018 he is a Tenured Associate Professor at EPFL.
Research project
This research project focuses on networks composed of ground Internet-of-Things (IoT) devices connected to flying drones. Connected IoT devices are expected to provide reliable and real-time communication for several applications such as smart farming, self-driving cars, and robotics. Besides, drones can provide cost-effective wireless communication solution when used as flying aerial base stations or gateways. Drones can be rapidly deployed to collect data from IoT devices, improve their connectivity and coverage, as well as provide them with wireless power.
The aim of this project is to investigate and demonstrate high capacity drones-assisted IoT networks with low energy consumption and highly secure communication. For this, efficient machine learning (ML) based algorithms for the maximisation of the capacity of IoT networks will be developed and demonstrated. At the same time, to alleviate the battery issue, intelligent methods for energy harvesting and power control will be developed. Moreover, the robustness capabilities of IoT networks to maintain high security levels against different kind of attacks and vulnerabilities will be investigated. Finally, we aim to bring these developments to fruition and enable their early exploitation by showing how to integrate them with the latest emerging IoT standards as shown in proof-of-concept demonstrators.
Publications / Science news
M. Jouhari, K. Ibrahimi, J. B. Othman and E. M. Amhoud, “Deep Reinforcement Learning-Based Energy Efficiency Optimization for Flying LoRa Gateways”, ICC 2023 – IEEE International Conference on Communications, Rome, Italy, 2023, pp. 6157-6162
Jouhari, M., Saeed, N., Alouini, M. S., & Amhoud, E. M. A survey on scalable LoRaWAN for massive IoT: Recent advances, potentials, and challenges. IEEE Communications Surveys & Tutorials. 2023
Delamou, M., Noubir, G., Dang, S., & Amhoud, E. M. An Efficient OFDM-Based Monostatic Radar Design for Multitarget Detection, 2023
Jouhari, M., Ibrahimi, K., Othman, J. B., & Amhoud, E. M. Deep Reinforcement Learning-based Energy Efficiency Optimization For Flying LoRa Gateways. ICC, 2023
Etiabi, Y., Jouhari, M., Burg, A., & Amhoud, E. M. (2023, June). Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning. In IEEE 97th Vehicular Technology Conference (VTC2023-Spring) (pp. 1-5), 2023
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