US US20260238967A1
[0000] A system for optimizing sensor activation scheduling in a distributed sensor network comprises a memory storing computer executable instructions and a processor configured to execute the instructions to receive measurements from sensors monitoring environmental parameters and implement an attention mechanism for computing relevance scores for the plurality of sensors. The processor is further configured to execute a reinforcement learning model to generate activation schedules for the plurality of sensors based on the relevance scores. The reinforcement learning model defines states of the sensors based on the computed relevance scores, sensor energy levels, or historical activation schedules of the sensors. The reinforcement learning model outputs actions representing subsets of sensors to activate at each time step that maximize a reward function that accounts for data quality, energy efficiency, and redundancy minimization. The processor is further configured to transmit activation signals to the selected subset of sensors.
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