{"id":"W4413417006","doi":"10.1109/smartnets65254.2025.11106385","title":"Cooperative Resource Allocation and Traffic Scheduling for IIoT Controllers in Edge Clouds: A Hierarchical Reinforcement Learning Approach","year":2025,"lang":"en","type":"article","venue":"","topic":"Network Time Synchronization Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Reinforcement learning; Computer science; Scheduling (production processes); Distributed computing; Enhanced Data Rates for GSM Evolution; Resource allocation; Computer network; Artificial intelligence; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000920967,0.0004245065,0.000580758,0.0002592799,0.0004578251,0.0006402242,0.001116354,0.0005523486,0.001059616],"category_scores_gemma":[0.001459887,0.0002224361,0.0002742786,0.0002310271,0.0006843096,0.0005926458,0.0007152256,0.0008243709,0.0001357588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091937,"about_ca_system_score_gemma":0.001688097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009043347,"about_ca_topic_score_gemma":0.008555195,"domain_scores_codex":[0.9995515,0.0001259997,0.00001619444,0.00009440719,0.00008635385,0.0001254846],"domain_scores_gemma":[0.9992994,0.0003008843,0.0001244697,0.00004093301,0.0001392559,0.00009496791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007650464,0.00008201233,0.0007531906,0.00002056647,0.00002048762,0.00004314284,0.00004923478,0.9664024,0.002120644,0.005447357,0.0004892551,0.02449512],"study_design_scores_gemma":[0.000002435167,0.00000928011,0.00003087596,6.536709e-7,0.000001515137,0.000002024434,0.000003652956,0.9991629,0.0001242869,0.0006124683,0.00004886929,0.000001001963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.058983,0.0001548621,0.9372249,0.0002826061,0.00003265966,0.00005714341,0.00001612517,0.000213681,0.003035006],"genre_scores_gemma":[0.9641151,0.00005675755,0.03427304,0.00009656249,0.0000231689,0.0000405406,0.00001495667,0.00001774973,0.001362019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009043347,"threshold_uncertainty_score":0.01798141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095524491516696,"score_gpt":0.2421043981593725,"score_spread":0.2311491532442056,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}