{"id":"W3033036004","doi":"10.1109/tnet.2020.2994015","title":"FoGMatch: An Intelligent Multi-Criteria IoT-Fog Scheduling Approach Using Game Theory","year":2020,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec en Outaouais","funders":"Lebanese American University","keywords":"Computer science; Distributed computing; Cloud computing; Internet of Things; Scheduling (production processes); Fog computing; Job shop scheduling; Latency (audio); Computer network; Mathematical optimization; Embedded system; Operating system","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":[0.001327943,0.001113141,0.001223078,0.0006824073,0.0007758111,0.001406384,0.001933705,0.001146129,0.002406785],"category_scores_gemma":[0.00138139,0.0004824221,0.001301923,0.0006789662,0.0008587292,0.001215214,0.001417715,0.001084342,0.0002378973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001564921,"about_ca_system_score_gemma":0.002323177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006929944,"about_ca_topic_score_gemma":0.006583213,"domain_scores_codex":[0.9991999,0.0003411452,0.00003107076,0.0001175041,0.0001677259,0.0001425964],"domain_scores_gemma":[0.9994769,0.0002661177,0.00006025015,0.00002629038,0.00008331721,0.00008719994],"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":[0.0000769938,0.00009943083,0.0004229816,0.00008366985,0.00009763775,0.00014974,0.00009176959,0.9184474,0.001918255,0.05384981,0.001650687,0.02311167],"study_design_scores_gemma":[0.00001075599,0.00002882422,0.00004922895,0.000005155605,0.00001010937,0.00001854361,0.00001692821,0.9888461,0.0001806727,0.01011985,0.0007067475,0.000007114837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007790547,0.0001346899,0.9875809,0.0001596438,0.00006145141,0.0001089034,0.0000396,0.0001044556,0.00401987],"genre_scores_gemma":[0.6846516,0.0003684749,0.3096501,0.0002706674,0.00008106382,0.0003026103,0.00009618584,0.00007433027,0.004505008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006929944,"threshold_uncertainty_score":0.01377922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1552664304438902,"score_gpt":0.3199529412789779,"score_spread":0.1646865108350877,"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."}}