{"id":"W4206186328","doi":"10.1109/jiot.2021.3139044","title":"Minimizing Age of Information in Multiaccess-Edge-Computing-Assisted IoT Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Age of Information Optimization","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scheduling (production processes); Edge computing; Network packet; Distributed computing; Optimization problem; Computer network; Mathematical optimization; Enhanced Data Rates for GSM Evolution; Algorithm","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":[0.001666759,0.001344613,0.001173874,0.0008230555,0.0007289988,0.001647782,0.001712425,0.001009942,0.001188355],"category_scores_gemma":[0.005571703,0.000704821,0.0005470862,0.001336634,0.001012328,0.00209243,0.001586011,0.001263708,0.0002134569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002067471,"about_ca_system_score_gemma":0.001753396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00400944,"about_ca_topic_score_gemma":0.003890311,"domain_scores_codex":[0.9991496,0.0002244937,0.00004633572,0.0002069218,0.0001964598,0.0001762666],"domain_scores_gemma":[0.9959757,0.002754482,0.0005331807,0.0001712401,0.0003702768,0.0001950382],"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":[0.00009190274,0.00005082273,0.0007813649,0.0001194099,0.00002382752,0.0001069702,0.00007958254,0.9597476,0.002210242,0.01295315,0.000820544,0.02301468],"study_design_scores_gemma":[0.000003295104,0.00002820346,0.0001159556,0.000006972071,0.00000784324,0.00002679533,0.00002207475,0.9932224,0.00074523,0.005575176,0.0002403658,0.000005713936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04513012,0.0009275115,0.9506391,0.0003473367,0.00008116449,0.0001050555,0.0001378456,0.0002218466,0.002409987],"genre_scores_gemma":[0.8642424,0.001238938,0.1307347,0.0001437838,0.00009385323,0.0001960239,0.0002137839,0.0001078881,0.003028654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00400944,"threshold_uncertainty_score":0.01500064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335723907737441,"score_gpt":0.2472261944592268,"score_spread":0.2338689553818524,"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."}}