{"id":"W2921337715","doi":"10.1109/tvt.2019.2905432","title":"Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer network; Computer science; Provisioning; Edge computing; Efficient energy use; Vehicular ad hoc network; Enhanced Data Rates for GSM Evolution; Service (business); Telecommunications; Distributed computing; Wireless ad hoc network; Engineering; Wireless; Electrical engineering; Business","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":[0.0007955874,0.0009415146,0.001015492,0.0004413482,0.0004654739,0.0007836911,0.001255908,0.0009665056,0.001180551],"category_scores_gemma":[0.001247307,0.0004666047,0.0005842482,0.0007280442,0.000507004,0.0007937222,0.001024119,0.001258224,0.0002080834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006300476,"about_ca_system_score_gemma":0.00113004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00472367,"about_ca_topic_score_gemma":0.0042537,"domain_scores_codex":[0.9997031,0.00009677983,0.00001573431,0.00007019917,0.00006750337,0.00004667326],"domain_scores_gemma":[0.9995136,0.0002333437,0.00006514636,0.00003149527,0.0001215747,0.00003479047],"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":[0.00002024601,0.00001486219,0.0001347565,0.00003102619,0.00001525375,0.0000298561,0.000021043,0.9832461,0.0007227734,0.003271443,0.0004913654,0.01200112],"study_design_scores_gemma":[0.000002883387,0.000006032233,0.00001215685,0.000001431781,0.000001707875,0.000003059758,0.000005281104,0.9988004,0.00008410267,0.0009683298,0.0001132516,0.000001347699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006620519,0.0001542743,0.991364,0.0001729909,0.0000402121,0.00002276258,0.00001978372,0.00009041332,0.001515074],"genre_scores_gemma":[0.7382202,0.0004874177,0.2560855,0.0002606417,0.0001175896,0.0002148778,0.0001842823,0.00007466029,0.004354822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00472367,"threshold_uncertainty_score":0.009392321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108243298740782,"score_gpt":0.2204915180944551,"score_spread":0.2096671882203769,"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."}}