{"id":"W2108884543","doi":"10.1109/sahcn.2008.41","title":"A Probability Model for Lifetime of Event-Driven Wireless Sensor Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Wireless sensor network; Computer science; Nondeterministic algorithm; Randomness; Network packet; Key distribution in wireless sensor networks; Event (particle physics); Computer network; Wireless network; Probability density function; Node (physics); Real-time computing; Wireless; Algorithm; Engineering; Telecommunications; Mathematics","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":[0.003432128,0.001487964,0.001135968,0.001388507,0.0007554171,0.001925678,0.003660469,0.002606202,0.003231054],"category_scores_gemma":[0.01360142,0.0006874566,0.0009392868,0.001830951,0.001855235,0.005144182,0.001071928,0.002337271,0.0009692804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001873233,"about_ca_system_score_gemma":0.000941442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003302482,"about_ca_topic_score_gemma":0.001689001,"domain_scores_codex":[0.9982223,0.0004934793,0.0001199258,0.0004006223,0.0005843781,0.0001792789],"domain_scores_gemma":[0.9933949,0.004625282,0.0007096985,0.0004598868,0.0006541609,0.0001561217],"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":[0.00006644286,0.00005106404,0.0006880943,0.0001652701,0.00003728956,0.0003519196,0.0002782433,0.5484583,0.003023208,0.4314557,0.002047378,0.01337696],"study_design_scores_gemma":[0.00001289067,0.00003889544,0.0001835652,0.00002152251,0.0000188754,0.0002080685,0.00002720832,0.9141892,0.0004872776,0.08181449,0.002971645,0.00002629953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006660968,0.0008106204,0.9884886,0.000466263,0.00008061808,0.00006108175,0.0002073501,0.000240278,0.002984231],"genre_scores_gemma":[0.8197331,0.005929558,0.1524786,0.000607589,0.0005214116,0.001085703,0.0008540387,0.0002583561,0.01853163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003660469,"threshold_uncertainty_score":0.01815104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03352908709137818,"score_gpt":0.2372401987072566,"score_spread":0.2037111116158784,"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."}}