PhD Position: Design and Optimization of LPWAN Relay Networks

This PhD position focuses on optimizing relay node deployment in Low-Power Wide-Area Networks (LPWAN), specifically LoRaWAN, to maximize network capacity and energy efficiency. The project, embedded in the ANR MORAL initiative, combines research in operational research, network optimization, and reinforcement learning. The successful candidate will develop mathematical models, design algorithms, and validate solutions through simulations and experimental platforms like WalT/WalToR/FeniX. The work involves collaboration between two research labs: LIG (Grenoble) and I3S (Sophia Antipolis), offering a multidisciplinary environment at the intersection of IoT and optimization.

• Develop mathematical models to optimize relay node placement and resource allocation in LoRaWAN networks.
• Formulate and solve mixed-integer linear programming (MILP) problems for network dimensioning and relay placement.
• Implement and test optimization techniques such as Lagrangian relaxation, Benders decomposition, and column generation.
• Explore reinforcement learning approaches to approximate optimal solutions for large-scale network instances.
• Validate models and algorithms through simulations using ns-3 and experimental testing on the WalT/WalToR/FeniX platform.
• Collaborate with researchers from two labs (LIG and I3S) and participate in joint meetings and fieldwork in Grenoble.
• Publish research findings in international conferences and journals related to networking and operational research.