{"id":"W2132135454","doi":"10.1109/glocom.2009.5425636","title":"Improving Neighbor Localization in Vehicular Ad Hoc Networks to Avoid Overhead from Periodic Messages","year":2009,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Beacon; Wireless ad hoc network; Overhead (engineering); Vehicular ad hoc network; Computer network; Global Positioning System; Software deployment; Bandwidth (computing); Position (finance); Node (physics); Distributed computing; Process (computing); Information exchange; Wireless; Telecommunications; Engineering","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":[0.00109067,0.0006418665,0.0007258097,0.0009573405,0.0005392177,0.0004742779,0.001116801,0.0005599267,0.0009062582],"category_scores_gemma":[0.005592036,0.0003037946,0.00036275,0.0007951963,0.0003024577,0.001855958,0.001018669,0.0003851202,0.0004238425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003788161,"about_ca_system_score_gemma":0.0004481742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00337658,"about_ca_topic_score_gemma":0.002730316,"domain_scores_codex":[0.9991365,0.0002373457,0.00004708455,0.000124171,0.0003580693,0.00009692776],"domain_scores_gemma":[0.9983014,0.0007683158,0.0001574069,0.0003363655,0.0003919795,0.00004439416],"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":[0.0002846523,0.0002133288,0.004405962,0.0002779252,0.00009742118,0.0002265602,0.0004215108,0.531633,0.0205958,0.01228488,0.003180813,0.4263782],"study_design_scores_gemma":[0.00005722287,0.0003335777,0.001493976,0.00002323307,0.00008926274,0.0002830953,0.0001019564,0.9758189,0.009360425,0.005534485,0.006872234,0.00003165492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0904402,0.002851079,0.9001724,0.0002567871,0.0001808129,0.00009318803,0.00004297085,0.002371153,0.003591443],"genre_scores_gemma":[0.7870418,0.001464612,0.2079431,0.0001216577,0.000119705,0.00008661637,0.0001774962,0.0001060083,0.002938954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00337658,"threshold_uncertainty_score":0.006713867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004318653461123565,"score_gpt":0.1933948984932429,"score_spread":0.1890762450321194,"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."}}