{"id":"W4253911219","doi":"10.1007/978-3-642-11723-7_13","title":"Computationally Efficient Mutual Entity Authentication in Wireless Sensor Networks","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mutual authentication; Computer science; Computer network; Authentication (law); Wireless sensor network; Wireless network; Wireless; Lightweight Extensible Authentication Protocol; Authentication protocol; Distributed computing; Computer security; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005285933,0.0003157364,0.0004093624,0.0002962387,0.0006869761,0.0002918409,0.002264842,0.0003846495,8.252201e-7],"category_scores_gemma":[0.00005419616,0.0002844649,0.0001831915,0.0003481371,0.0005401751,0.0002677585,0.0008749592,0.0008662058,9.686399e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009327164,"about_ca_system_score_gemma":0.0001624829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003281583,"about_ca_topic_score_gemma":0.0001673363,"domain_scores_codex":[0.9982523,0.00002158188,0.0008054943,0.0002410292,0.0003718437,0.0003077058],"domain_scores_gemma":[0.9978186,0.0005879235,0.0005774589,0.0007050684,0.0002526864,0.00005824895],"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":[0.000001147619,0.00002058087,0.00000623147,0.00006439068,0.0000274673,1.251913e-7,0.001237237,0.9044691,0.00001530745,0.08973372,0.000006376116,0.004418368],"study_design_scores_gemma":[0.0002104401,0.00002585279,0.0002592805,0.000181199,0.00002176801,0.0000116139,0.000002494817,0.9964889,0.00004165311,0.0008447983,0.001618272,0.0002936882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006669749,0.0001304374,0.990495,0.0007028765,0.0008494363,0.000638116,0.00002705522,0.0000807699,0.0004065426],"genre_scores_gemma":[0.2231406,0.00005242653,0.7763852,0.0001434414,0.0001665484,0.00002001017,0.00005262567,0.00001864828,0.00002044652],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2164709,"threshold_uncertainty_score":0.9999608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415940039881364,"score_gpt":0.2277223478870772,"score_spread":0.2135629474882635,"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."}}