{"id":"W3159010613","doi":"10.1109/sagc50777.2020.00014","title":"Distributed Task Offloading and Resource Allocation in Vehicular Edge Computing","year":2020,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Benchmark (surveying); Resource allocation; Computation offloading; Distributed computing; Computational complexity theory; Mathematical optimization; Edge computing; Quality of service; Task (project management); Computation; Enhanced Data Rates for GSM Evolution; Optimization problem; Bandwidth (computing); Resource management (computing); Mobile edge computing; Integer programming; Server; Computer network; Algorithm; Engineering; Mathematics; Artificial intelligence","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":[0.0004247534,0.0006937396,0.0007978756,0.0003142231,0.0006932238,0.0007777002,0.001028124,0.0005500218,0.00103223],"category_scores_gemma":[0.0006738575,0.0002958663,0.0003698824,0.0006149963,0.0004683915,0.000885469,0.0009918597,0.000531162,0.0001498198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006012351,"about_ca_system_score_gemma":0.001149352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006061534,"about_ca_topic_score_gemma":0.007298665,"domain_scores_codex":[0.9995905,0.00009147544,0.00001626034,0.0001072002,0.00006693917,0.0001277216],"domain_scores_gemma":[0.9997744,0.0000865731,0.00002775944,0.00003101891,0.00004315368,0.00003704792],"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":[0.0001632144,0.00007023897,0.0005225996,0.00007222814,0.00002551137,0.0001245687,0.0000825168,0.9323595,0.004748541,0.007772373,0.001629725,0.05242896],"study_design_scores_gemma":[0.000006546166,0.00002065824,0.00008678983,0.000001995139,0.000003951608,0.00001763784,0.00002526629,0.9966061,0.0005444367,0.002301496,0.0003815687,0.000003600074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08399463,0.0007064792,0.9102715,0.0002212387,0.00009107767,0.00006427075,0.00005540601,0.0002600065,0.004335301],"genre_scores_gemma":[0.9382649,0.0002296318,0.05936243,0.0000752275,0.00003321579,0.00007421223,0.00007145174,0.00003403118,0.001854969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006061534,"threshold_uncertainty_score":0.01205248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01694823626908676,"score_gpt":0.2219952261998157,"score_spread":0.2050469899307289,"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."}}