{"id":"W2089609964","doi":"10.1109/twc.2012.030812.110493","title":"Distributed Scheduling in Multihop Wireless Networks with Maxmin Fairness Provisioning","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer network; Maximum throughput scheduling; Fairness measure; Multipath routing; Provisioning; Scheduling (production processes); Distributed computing; Max-min fairness; Proportionally fair; Network packet; Fair queuing; Wireless network; Wireless; Dynamic Source Routing; Routing protocol; Dynamic priority scheduling; Throughput; Round-robin scheduling; Resource allocation; Quality of service; Mathematical optimization; 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":[0.005330243,0.0006987459,0.001107268,0.0004748789,0.001309714,0.001446608,0.001811419,0.0008832365,0.0007052785],"category_scores_gemma":[0.006953808,0.0004169503,0.0004079521,0.0008021096,0.001501884,0.002023346,0.001153856,0.0009953798,0.000171063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002238252,"about_ca_system_score_gemma":0.002102344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001694361,"about_ca_topic_score_gemma":0.002124184,"domain_scores_codex":[0.9979486,0.001042179,0.00007797502,0.0002979528,0.0004457115,0.0001876194],"domain_scores_gemma":[0.9971313,0.001807695,0.0003889363,0.0003219104,0.0002264705,0.0001236225],"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":[0.0002498493,0.00008849269,0.0008131593,0.0001130343,0.00004455573,0.0001241828,0.0001978055,0.8352728,0.00410013,0.111688,0.0008792893,0.04642861],"study_design_scores_gemma":[0.00002164956,0.00004920676,0.00009668732,0.000007667047,0.000008225612,0.00003369546,0.0000177324,0.9625309,0.00119407,0.03512766,0.0009026076,0.000009896523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02386036,0.0005887172,0.9730929,0.0002316149,0.00006030459,0.00005680353,0.00002048067,0.0001306386,0.001958197],"genre_scores_gemma":[0.8239959,0.0004742962,0.1733013,0.00013158,0.0001542963,0.0001388122,0.0000252193,0.00005328204,0.001725196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005330243,"threshold_uncertainty_score":0.02818942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03719978931620121,"score_gpt":0.2828541261601898,"score_spread":0.2456543368439886,"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."}}