Self-healing communication for mobile IoT systems
I designed and implemented a self-healing routing system for mobile wireless sensor networks using reinforcement learning.
The system adapts in real time to node mobility, unstable connections, and battery constraints, keeping the network operational even under dynamic and failure-prone conditions.
Autonomous routing under real-world constraints
This project demonstrates how reinforcement learning can be combined with high-fidelity simulation to solve routing problems in dynamic, battery-constrained networks.
By treating the network as a living system rather than a static graph, the solution adapts continuously to movement, failures, and energy limitations, resulting in higher reliability and longer operational lifetime.
