{"id":"W2358248302","doi":"10.1109/tifs.2016.2566446","title":"Delay-Aware Optimization of Physical Layer Security in Multi-Hop Wireless Body Area Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Physical layer; Computer network; Wireless sensor network; Network topology; Nash equilibrium; Topology (electrical circuits); Node (physics); Distributed computing; Wireless; Mathematical optimization; Mathematics; Telecommunications","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":[0.001487566,0.0009471867,0.0006966448,0.0004711681,0.0004110681,0.0009841997,0.0008951387,0.0009168792,0.0007743167],"category_scores_gemma":[0.003200698,0.0004491572,0.0003185324,0.0005033757,0.001296623,0.001333677,0.00113742,0.0006471986,0.0001089935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019049,"about_ca_system_score_gemma":0.00111132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001468764,"about_ca_topic_score_gemma":0.001515238,"domain_scores_codex":[0.9993523,0.0002868079,0.00001873566,0.0000867959,0.0001448744,0.0001105239],"domain_scores_gemma":[0.9984011,0.001125227,0.0001964387,0.00006403455,0.0001342391,0.00007902431],"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":[0.00003240615,0.00001618726,0.0001887412,0.00001928516,0.00001032932,0.00002937039,0.00001926312,0.9893405,0.001173775,0.004902099,0.00007844154,0.004189602],"study_design_scores_gemma":[0.000005654832,0.00003934357,0.00007188359,0.000002390459,0.000004294374,0.00001358482,0.000012619,0.9965585,0.0004057089,0.002772764,0.0001095829,0.00000371068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1027193,0.0006071327,0.8931327,0.000309026,0.00005796724,0.00004717866,0.00003138487,0.00006220774,0.00303305],"genre_scores_gemma":[0.9688062,0.0003035291,0.02917457,0.00003055757,0.00002200641,0.00003579435,0.00001421952,0.00001985847,0.001593209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001487566,"threshold_uncertainty_score":0.007867098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267830787162267,"score_gpt":0.2320039779186135,"score_spread":0.2193256700469908,"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."}}