{"id":"W3037950529","doi":"10.1109/twc.2020.3003775","title":"Covert Localization in Wireless Networks: Feasibility and Performance Analysis","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Covert; Computer science; Transmitter power output; Wireless; Noise (video); Power (physics); Transmission (telecommunications); Statistical power; Mathematical optimization; Wireless network; Algorithm; Mathematics; Artificial intelligence; Telecommunications; Statistics; Transmitter","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004634502,0.001486056,0.001407237,0.001392315,0.0009109592,0.001782687,0.001561424,0.001988483,0.002001234],"category_scores_gemma":[0.02843353,0.000579383,0.0008338543,0.001543879,0.002929444,0.004020331,0.003121376,0.001841221,0.0003550261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002020417,"about_ca_system_score_gemma":0.001256896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001488293,"about_ca_topic_score_gemma":0.0008429286,"domain_scores_codex":[0.9961462,0.001563788,0.0001031609,0.0005208318,0.001214745,0.0004513072],"domain_scores_gemma":[0.9798653,0.01532741,0.001643998,0.0009983372,0.001839801,0.0003251265],"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":[0.0001682711,0.00004373084,0.0008959919,0.0002448162,0.00003902542,0.0002072475,0.0001236116,0.9250124,0.002777752,0.04289558,0.001107263,0.02648436],"study_design_scores_gemma":[0.000008008731,0.00008103975,0.0001296606,0.0000202363,0.000007939483,0.000119894,0.00002710378,0.9883022,0.0008907978,0.01004856,0.000354837,0.000009641543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02405445,0.001533873,0.9680805,0.0008148505,0.00005662941,0.00008329788,0.00006143958,0.0001975942,0.005117405],"genre_scores_gemma":[0.9102997,0.002776969,0.08441768,0.0002313235,0.0001724975,0.0002783283,0.0001531845,0.00009466646,0.001575668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004634502,"threshold_uncertainty_score":0.02450985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02643972454580292,"score_gpt":0.2406094986817048,"score_spread":0.2141697741359019,"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."}}