{"id":"W4413808937","doi":"10.1002/dac.70239","title":"ReLeC‐MEO: Reinforcement Learning‐Based Clustering With Multi‐Objective Efficient Optimization for Energy‐Efficient IoT Networks","year":2025,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Reinforcement learning; Cluster analysis; Internet of Things; Artificial intelligence; Efficient energy use; Machine learning; Distributed computing; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001097732,0.0002221081,0.0003241588,0.0005946227,0.000274095,0.0004071769,0.002102556,0.0001154792,0.000002600298],"category_scores_gemma":[0.0001238495,0.0001945884,0.0001565906,0.0005472564,0.00007288316,0.0001356359,0.0002682007,0.0003295604,6.833899e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005584812,"about_ca_system_score_gemma":0.0002297409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006007502,"about_ca_topic_score_gemma":0.00001492839,"domain_scores_codex":[0.9973229,0.0003826757,0.001003014,0.000268708,0.0007601142,0.0002626013],"domain_scores_gemma":[0.9946346,0.0006758894,0.001342064,0.0006408779,0.00262118,0.00008541108],"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":[0.0001914397,0.0001648637,0.00007739541,0.000006272259,0.0001773494,0.000003417575,0.0002156664,0.985854,0.00002192349,0.01186398,0.000104498,0.001319221],"study_design_scores_gemma":[0.001980647,0.0001531741,0.00005513128,0.0008094874,0.00002527758,0.00002358681,0.0001356987,0.9926716,0.0002091951,0.000003974738,0.003753813,0.000178382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001391814,0.001154627,0.9943772,0.0008350536,0.001336144,0.0003016051,8.216475e-7,0.00007713381,0.0005256216],"genre_scores_gemma":[0.9362566,0.0001040833,0.06293207,0.0002129613,0.0001083404,0.00005843521,0.00002081254,0.00002034564,0.0002862866],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9348649,"threshold_uncertainty_score":0.7935086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156438439684886,"score_gpt":0.2610144051719789,"score_spread":0.2494500207751301,"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."}}