{"id":"W2782772926","doi":"10.1109/glocom.2017.8253986","title":"An Efficient Compromised Node Revocation Scheme in Fog-Assisted Vehicular Crowdsensing","year":2017,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Revocation; Computer science; Computer network; Key escrow; Scalability; Computer security; Node (physics); Protocol (science); Public-key cryptography; Encryption; Overhead (engineering); Engineering; Database","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":[0.0008908755,0.0004715511,0.0008196731,0.0005370235,0.0008451198,0.0006899117,0.00191833,0.0008952586,0.0005331269],"category_scores_gemma":[0.001955129,0.0002022027,0.0005894661,0.0004516024,0.0007754971,0.002059055,0.001819037,0.0005699052,0.0002262095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007035371,"about_ca_system_score_gemma":0.0008094416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001374379,"about_ca_topic_score_gemma":0.001233614,"domain_scores_codex":[0.9989308,0.0002555806,0.00009011874,0.0002467315,0.0003086021,0.0001681369],"domain_scores_gemma":[0.9990503,0.0001598473,0.0001592401,0.0003411802,0.0002149408,0.0000744637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001946779,0.0003076361,0.007078116,0.0007996824,0.0004086897,0.00359318,0.002456365,0.2411207,0.2841427,0.09580455,0.005612652,0.3567289],"study_design_scores_gemma":[0.0001294901,0.0007991392,0.001670738,0.00005582107,0.0001573304,0.002127195,0.0005418799,0.9030404,0.05481062,0.01817244,0.01832606,0.0001688801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1340742,0.0009647409,0.8591233,0.0003180874,0.0002677257,0.0002970014,0.00008926841,0.0007730892,0.004092568],"genre_scores_gemma":[0.9603037,0.0001900417,0.03788811,0.00008129652,0.00002078078,0.00005731085,0.00005787967,0.00001547856,0.001385417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00191833,"threshold_uncertainty_score":0.005104601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152342068524449,"score_gpt":0.2531728643005512,"score_spread":0.2379386574481063,"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."}}