Since IoT networks include a large number of connected devices that must be controlled, classic optimization and control algorithms are not capable anymore to control and to make efficient decisions. Indeed, not only the network is large-scaled but also the deployed protocols and softwares are becoming more and more complex. Classic algorithms reduce this complexity usually by fixing some parameters intuitively and/or by using strong assumptions regarding especially the objective functions. Deep Reinforcement Learning is a powerful tool that can be used in order to analyze network data and reach dynamically optimal configurations. Several previous works have shown the potential of DRL in different types of networks. The objective of the thesis is to explore how DRL can be applied and improved to provide faster decisions in IoT networks where the performance in terms of transmission, reliability and energy consumption is very sensitive to network parameters.
Keywords : Fast DRL, ANN, Q-Learning, Internet-of-Things
This PhD research project has been submitted for a funding request to “Sorbonne Center for Artificial Intelligence (SCAI)”. The PhD candidate selected by the project leader will therefore participate in the project selection process (including a file and an interview) to obtain funding.