{"id":"W2135186590","doi":"10.1109/icccn.2011.6005768","title":"Markov Modeling of Fault-Tolerant Wireless Sensor Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Mean time between failures; Fault tolerance; Computer science; Reliability (semiconductor); Node (physics); Distributed computing; Key distribution in wireless sensor networks; Reliability engineering; Leverage (statistics); Markov model; Computer network; Embedded system; Wireless; Markov chain; Real-time computing; Wireless network; Engineering; Failure rate; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003043289,0.0002339805,0.0003330595,0.0001267338,0.00009091961,0.00004218275,0.001136094,0.0001548595,0.00005275597],"category_scores_gemma":[0.000008292791,0.0002036546,0.0001336286,0.0005147409,0.00006754661,0.000266689,0.0003346674,0.000214258,0.00001528702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002406794,"about_ca_system_score_gemma":0.00003000388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002087514,"about_ca_topic_score_gemma":0.00002710704,"domain_scores_codex":[0.998,0.0000949225,0.0005025466,0.0005197836,0.0003499271,0.0005328348],"domain_scores_gemma":[0.998489,0.00007930541,0.0001470383,0.0009516989,0.0001856955,0.0001472285],"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":[0.00001535282,0.0001053886,0.0002266154,0.000008202048,0.00002270776,0.0000205177,0.0004284059,0.9589977,0.0001438283,0.03044287,0.0000954188,0.00949296],"study_design_scores_gemma":[0.0002436799,0.00004513629,0.00008226329,0.00004179241,0.000007022399,0.00001535267,0.00005404552,0.9978034,0.001313749,0.0001062608,0.00003314648,0.0002541105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1123942,0.0001042939,0.8656558,0.00004023009,0.0004757321,0.000103302,4.600629e-7,0.0002797947,0.02094619],"genre_scores_gemma":[0.8549303,0.0000412981,0.1444544,0.0001511582,0.00006893137,0.000005504347,0.000001424246,0.00002124184,0.0003257185],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7425361,"threshold_uncertainty_score":0.8304793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02473053902837073,"score_gpt":0.2104652506899096,"score_spread":0.1857347116615388,"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."}}