{"id":"W4210970635","doi":"10.1109/jiot.2022.3150386","title":"Joint Online Optimization of Data Sampling Rate and Preprocessing Mode for Edge–Cloud Collaboration-Enabled Industrial IoT","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Joint (building); Cloud computing; Preprocessor; Enhanced Data Rates for GSM Evolution; Mode (computer interface); Sampling (signal processing); Data pre-processing; Data mining; Real-time computing; Artificial intelligence; Telecommunications; Human–computer interaction; Operating system; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007335875,0.000968931,0.0009402847,0.0002849058,0.0004081906,0.000943429,0.0009874306,0.00068336,0.0009525719],"category_scores_gemma":[0.001726857,0.0003451522,0.0004568186,0.000494381,0.0004299212,0.0009562055,0.0009887828,0.0007857539,0.0001462952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006091092,"about_ca_system_score_gemma":0.001242248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00387384,"about_ca_topic_score_gemma":0.003369989,"domain_scores_codex":[0.999501,0.00008606069,0.00002543061,0.0001500898,0.0001143831,0.0001230452],"domain_scores_gemma":[0.9994059,0.0002808339,0.000118417,0.00004062979,0.00009807813,0.00005617],"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":[0.000197103,0.0001415983,0.001357183,0.0001059097,0.00002761429,0.0001164873,0.00007130815,0.9414437,0.006900724,0.004538522,0.0008211737,0.04427857],"study_design_scores_gemma":[0.000004136376,0.00001940578,0.000114566,0.000001988664,0.000004273393,0.000007970778,0.000008893851,0.9986711,0.0005185391,0.0005702146,0.00007612907,0.000002759241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04658586,0.0002925459,0.950536,0.0001487375,0.00003137962,0.00005442997,0.00003399553,0.0002477334,0.00206934],"genre_scores_gemma":[0.9658708,0.0001460833,0.03304444,0.00005085782,0.00001613181,0.00006344685,0.00004519947,0.00002299855,0.000740151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00387384,"threshold_uncertainty_score":0.007702529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1182272898667195,"score_gpt":0.3304883888709186,"score_spread":0.2122610990041991,"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."}}