Deterministic Mesh Node Synchronization via Multi-Agent Reinforcement Learning

The patent implements a feedback mechanism where each node continuously measures synchronization offsets with its neighbors, estimates future offsets using linear regression, and applies corrections based on a catch-up factor. This closed-loop feedback system maintains network determinism by constantly adjusting for clock drift and synchronization errors without requiring complex centralized control.

Each node in the mesh network autonomously performs synchronization measurements, estimates its own offset using linear regression on historical data, and applies corrections to its local clock independently. This self-service approach eliminates the need for complex centralized synchronization management while maintaining network determinism through distributed autonomous operation.

The patent applies partial correction by using a catch-up factor (alpha) that controls the fraction of estimated offset applied to the local clock. This prevents excessive correction that would cause instability while still improving synchronization precision. The linear regression uses a limited history of offset measurements rather than all past data, balancing precision with computational efficiency.

The synchronization system dynamically adapts to changing network conditions by continuously updating the linear regression model with new offset measurements and adjusting the catch-up factor based on observed synchronization behavior. This dynamic adaptation improves precision without requiring excessive computational power by processing only necessary updates.