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Before diving into BRIGHT, its important to position our work relative to the existing landscape. Previous research has largely focused on two areas: either optimizing the measurement process itself, or applying machine learning to traffic prediction. For instance, some work focuses on low-cost monitoring techniques, but these often lack the adaptability needed for dynamic environments. Other adaptive methods exist, but they typically rely on fixed heuristics or simple rate-based thresholds, meaning they cant effectively quantify or manage measurement uncertainty. Our key contribution is bridging this gap. We are the first to integrate Bayesian state estimation—which explicitly models uncertainty—with a reinforcement learning policy for dynamic resource allocation. This unified approach allows BRIGHT to continuously learn the optimal monitoring strategy based on the current network state and the inherent uncertainty of our estimates. This integration is what sets BRIGHT apart and allows us to model monitoring as a sequential decision process.
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IEEE/ICACT20260358 Slide.03
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