{"id":"W2991291458","doi":"10.1002/itl2.141","title":"Fog‐enabled vehicular networks: A new challenge for mobility management","year":2019,"lang":"en","type":"article","venue":"Internet Technology Letters","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Computer science; Latency (audio); Mobility management; Vehicular ad hoc network; Architecture; Computer network; Enhanced Data Rates for GSM Evolution; Intelligent transportation system; Edge computing; Mobility model; Vehicular communication systems; Telecommunications; Wireless ad hoc network; Engineering; Transport engineering; Wireless; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002005621,0.0003601815,0.0004125267,0.0002918012,0.00002559317,0.00003751335,0.000708615,0.0003874521,0.0001130473],"category_scores_gemma":[0.000008683017,0.000383263,0.0001795641,0.0003122213,0.00007455461,0.0001135982,0.0001944928,0.0004853013,0.0002658243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002297292,"about_ca_system_score_gemma":0.000006546548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001265833,"about_ca_topic_score_gemma":0.00003057967,"domain_scores_codex":[0.9980733,0.00001758909,0.0003694582,0.0005584533,0.0001490493,0.0008321623],"domain_scores_gemma":[0.9989004,0.00004484598,0.00005677654,0.0008758071,0.00002490512,0.00009731502],"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":[0.00003567565,0.00004683524,0.0007021074,0.0001939405,0.0005776852,0.00008411915,0.000059227,0.9182696,0.001285236,0.00390708,0.05908871,0.01574973],"study_design_scores_gemma":[0.001597118,0.0001343004,0.0001838393,0.0001601949,0.00008070162,0.00003061662,0.00004930693,0.8264173,0.001140312,0.001167356,0.1684733,0.0005656962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.694751,0.001778046,0.2910209,0.00472393,0.001475771,0.002141208,0.000003678325,0.002693442,0.001412074],"genre_scores_gemma":[0.9941325,0.0001034388,0.003662497,0.0007956377,0.0001762582,0.00030927,0.00002327651,0.0001077571,0.0006893701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2993816,"threshold_uncertainty_score":0.999862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004654811525561949,"score_gpt":0.1889142030639698,"score_spread":0.1842593915384079,"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."}}