RL-ASL uses reinforcement learning to adaptively skip listening slots in TSCH networks, delivering up to 46% lower power consumption and 96% lower latency with near-perfect reliability.
6G Internet of Things: A Comprehensive Survey
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
GeoUQ-GFNet reconstructs dense urban gain radio maps from sparse measurements using geometry priors and uncertainty-guided active sensing, showing consistent gains over non-adaptive sampling on the new UrbanRT-RM ray-tracing benchmark.
citing papers explorer
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RL-ASL: A Dynamic Listening Optimization for TSCH Networks Using Reinforcement Learning
RL-ASL uses reinforcement learning to adaptively skip listening slots in TSCH networks, delivering up to 46% lower power consumption and 96% lower latency with near-perfect reliability.
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Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection
GeoUQ-GFNet reconstructs dense urban gain radio maps from sparse measurements using geometry priors and uncertainty-guided active sensing, showing consistent gains over non-adaptive sampling on the new UrbanRT-RM ray-tracing benchmark.