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.
Using IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the Internet of Things (IoT): Problem Statement
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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.