{"id":"W4401567462","doi":"10.1109/jiot.2024.3443701","title":"Energy-Efficient Joint Optimization of Sensing and Computation in MEC-Assisted IoT Using Mean-Field Game","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Toyota Motor Corporation; National Natural Science Foundation of China","keywords":"Computer science; Server; Energy consumption; Computation offloading; Distributed computing; Computation; Efficient energy use; Computational complexity theory; Optimization problem; Edge computing; Mobile edge computing; Markov decision process; Field (mathematics); Internet of Things; Enhanced Data Rates for GSM Evolution; Markov process; Computer network; Artificial intelligence; Algorithm; Embedded system","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":[0.0006322304,0.0001089385,0.0002036607,0.0004475697,0.00002526998,0.0002489642,0.0001985852,0.00006387472,0.000005076544],"category_scores_gemma":[0.00006167815,0.0001015718,0.00006594416,0.0002775367,0.0000344524,0.0002920535,0.0001006306,0.0002788222,3.641459e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001137617,"about_ca_system_score_gemma":0.0000640362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001936838,"about_ca_topic_score_gemma":0.000002019068,"domain_scores_codex":[0.9986043,0.00008755626,0.0006188414,0.0001702958,0.0003679398,0.0001510886],"domain_scores_gemma":[0.9992011,0.0001312185,0.000368147,0.00009777072,0.0001530173,0.00004878953],"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":[0.00000822906,0.0000125361,0.00002181642,0.00004695169,0.00002752526,0.00002899783,0.003834002,0.9729222,0.008348951,0.0004076003,0.00003064956,0.01431055],"study_design_scores_gemma":[0.000190031,0.0001184725,0.00003609201,0.001052813,0.0000131476,0.0003659697,0.00006028408,0.9794357,0.01847185,0.000158991,0.000008342744,0.00008832437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1286228,0.0001264837,0.8700819,0.0001210403,0.0008577808,0.00003525687,7.939688e-8,0.00002471694,0.0001298703],"genre_scores_gemma":[0.8295692,0.00001283371,0.1702856,0.00006209456,0.00003162937,8.812484e-8,2.839709e-7,0.000008425809,0.00002982401],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7009463,"threshold_uncertainty_score":0.4141978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03143482696755857,"score_gpt":0.2754179182907363,"score_spread":0.2439830913231777,"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."}}