{"id":"W3215243218","doi":"10.1109/tvt.2021.3131395","title":"A Double Auction Mechanism for Resource Allocation in Coded Vehicular Edge Computing","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada); University of British Columbia","funders":"Ministry of Education - Singapore; National Research Foundation Singapore","keywords":"Double auction; Computer science; Server; Cloud computing; Incentive compatibility; Auction algorithm; Edge computing; Computer network; Resource allocation; Distributed computing; Computation offloading; Incentive; Bidding; Auction theory; Operating system; Microeconomics; Revenue equivalence","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":[0.003105453,0.0008748898,0.001196044,0.0009935503,0.001019557,0.00261489,0.002740108,0.001315942,0.003556627],"category_scores_gemma":[0.00591998,0.0004909853,0.0008448625,0.001291568,0.001458444,0.003335719,0.001568313,0.001218109,0.0004360024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001835806,"about_ca_system_score_gemma":0.002446546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002214683,"about_ca_topic_score_gemma":0.001591471,"domain_scores_codex":[0.9969292,0.001304483,0.0001715537,0.0004270018,0.0006090691,0.000558617],"domain_scores_gemma":[0.9971604,0.001233591,0.000346923,0.0003972375,0.0005315711,0.0003302514],"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":[0.0007985753,0.0002592041,0.0009138656,0.0002894057,0.0001383535,0.0005785956,0.0002879159,0.5248858,0.01131501,0.4014423,0.00400518,0.0550858],"study_design_scores_gemma":[0.0001312506,0.0001692241,0.0001506956,0.00001857359,0.0000301968,0.0002258719,0.00006175866,0.9277521,0.001894494,0.0665721,0.002938141,0.0000556404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04594,0.0002762721,0.9450275,0.0002532719,0.0001053179,0.0002640666,0.0001003437,0.0002517359,0.007781549],"genre_scores_gemma":[0.9201736,0.0002030206,0.07450502,0.00009570553,0.00003639354,0.0002409047,0.00006257528,0.00004109136,0.004641708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003556627,"threshold_uncertainty_score":0.01642334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601390792192767,"score_gpt":0.2684394144233507,"score_spread":0.2424255065014231,"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."}}