{"id":"W2915289913","doi":"10.1109/glocom.2018.8647598","title":"Differentiated QoS to Heterogeneous IoT Nodes in IEEE 802.11ah RAW Mechanism","year":2018,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer network; Computer science; Quality of service; IEEE 802.1X; Protocol (science); Heterogeneous network; Access control; Internet of Things; Media access control; Service set; Throughput; Distributed computing; Wireless network; IEEE 802.11; Wireless; Wi-Fi; Computer security; Telecommunications","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":[0.003633334,0.000673642,0.0004649528,0.0006841499,0.000497533,0.001359065,0.001095428,0.0006002849,0.0007627422],"category_scores_gemma":[0.00586232,0.000233207,0.0003966105,0.0004501493,0.0008141479,0.001537529,0.0009966495,0.0007773078,0.0001196118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006900819,"about_ca_system_score_gemma":0.0005363439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000623744,"about_ca_topic_score_gemma":0.0004054468,"domain_scores_codex":[0.9978776,0.0005858791,0.000160599,0.0002160233,0.0009023892,0.0002574773],"domain_scores_gemma":[0.9958768,0.001867907,0.0004745637,0.0006663488,0.0009803415,0.0001340165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002032529,0.0008189997,0.02579416,0.0009712349,0.0003351629,0.001547236,0.001012636,0.2492264,0.4037476,0.1381431,0.002651454,0.1737194],"study_design_scores_gemma":[0.0001479831,0.002957083,0.01336875,0.0001134835,0.0002783348,0.001628978,0.0004995221,0.8201129,0.1299139,0.02366678,0.007117304,0.0001949326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6235117,0.001630707,0.3636233,0.0003452149,0.000241934,0.0003204012,0.0001291753,0.000721295,0.00947617],"genre_scores_gemma":[0.9760991,0.0001749175,0.02298489,0.00007181388,0.00002792301,0.0000774545,0.00004365351,0.00001830919,0.0005019548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003633334,"threshold_uncertainty_score":0.01921517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192832252851728,"score_gpt":0.2683627503296905,"score_spread":0.2490795250445177,"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."}}