{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001110714,0.001001065,0.001398308,0.0003584332,0.0004258639,0.0009553618,0.001091637,0.001203261,0.00150587],"category_scores_gemma":[0.002530408,0.0005092978,0.0006037031,0.0003920072,0.00114456,0.0009932384,0.001037558,0.001073689,0.0001432358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071278,"about_ca_system_score_gemma":0.001358906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006613898,"about_ca_topic_score_gemma":0.004852817,"domain_scores_codex":[0.9994708,0.0001917122,0.00001880311,0.0001111021,0.00008793738,0.0001197029],"domain_scores_gemma":[0.9986749,0.0009431131,0.0001156238,0.00003807196,0.0001253649,0.0001030183],"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":[0.0000527882,0.00002416391,0.000343431,0.0000278717,0.00001985593,0.00007306089,0.00002104324,0.9883301,0.0006112639,0.00697,0.0004029662,0.003123454],"study_design_scores_gemma":[0.000005655099,0.000009998645,0.00004050815,0.000001631706,0.000002933063,0.000006012684,0.000003836103,0.9979942,0.00005777758,0.001819346,0.00005601438,0.000002160874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08071963,0.0004112697,0.9105447,0.0007490545,0.00009198318,0.00008924273,0.0000975826,0.0001854965,0.007110988],"genre_scores_gemma":[0.9787301,0.0001338383,0.01872638,0.0001131009,0.00001699179,0.0000857549,0.00003922531,0.00002000078,0.002134506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006613898,"threshold_uncertainty_score":0.01315081,"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."}}