{"id":"W2186117074","doi":"10.1109/pimrc.2015.7343496","title":"A dynamic access class barring scheme to balance massive access requests among base stations over the cellular M2M networks","year":2015,"lang":"en","type":"article","venue":"","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer network; Random access; Base station; Aloha; Cellular network; Throughput; Network congestion; Radio access network; Scheme (mathematics); Class (philosophy); Access control; Access network; Telecommunications; Wireless; Network packet","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":[0.0007242709,0.0004175213,0.0004864905,0.0006652371,0.001022085,0.0007312867,0.001507608,0.0003697253,0.001016331],"category_scores_gemma":[0.001590255,0.0001699685,0.0003009129,0.0004806323,0.0005696567,0.0008111263,0.0008073539,0.0006415228,0.0001959422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000628133,"about_ca_system_score_gemma":0.0008119042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002595555,"about_ca_topic_score_gemma":0.003125851,"domain_scores_codex":[0.9995043,0.0001303229,0.00003076583,0.00009461698,0.0001285662,0.0001113846],"domain_scores_gemma":[0.9990137,0.0003254127,0.0001327976,0.0002143741,0.000199738,0.000114005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001206831,0.0006439381,0.004673845,0.0002496438,0.0002043667,0.001030611,0.0007031736,0.2665084,0.3107579,0.0658743,0.004865597,0.3432814],"study_design_scores_gemma":[0.00004332908,0.000417612,0.0009586518,0.00001276811,0.00006563762,0.0004607151,0.00007013512,0.9670871,0.02279876,0.003478687,0.004550685,0.00005589815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1424655,0.0004852846,0.8513694,0.0001701973,0.0001132492,0.0001970771,0.00005787132,0.001497021,0.003644402],"genre_scores_gemma":[0.9641094,0.00008405029,0.03468813,0.00004670797,0.00003899758,0.00005374885,0.0000270067,0.00001965046,0.0009324562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002595555,"threshold_uncertainty_score":0.005160868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357976209995413,"score_gpt":0.2964066889589607,"score_spread":0.2728269268590066,"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."}}