{"id":"W2115488979","doi":"10.1109/wocn.2011.5872918","title":"Increasing the reliability of wireless sensor network with a new testing approach based on compressed sensing theory","year":2011,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wireless sensor network; Compressed sensing; Key distribution in wireless sensor networks; Computer science; Reliability (semiconductor); Wireless; Wireless network; Wi-Fi array; Mobile wireless sensor network; Real-time computing; Computer network; Telecommunications; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0005489548,0.0002197972,0.0002686332,0.00004579691,0.0001025438,0.00002330376,0.0001735991,0.00007499025,0.00001015337],"category_scores_gemma":[0.0001086262,0.0001375538,0.00005119326,0.0003128412,0.000121069,0.00005184723,0.00003467024,0.0002467364,0.000001184981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002458976,"about_ca_system_score_gemma":0.00003440886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005644762,"about_ca_topic_score_gemma":0.000007385903,"domain_scores_codex":[0.9988815,0.0001736587,0.0002517325,0.0002240458,0.0001858677,0.000283246],"domain_scores_gemma":[0.9983048,0.0007885205,0.00007816934,0.0006305116,0.0001343635,0.00006356223],"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":[0.0007746128,0.0001363366,0.008565195,0.0001111324,0.0001067454,0.00001740451,0.0009043391,0.9477463,0.01604639,0.002200103,0.001152953,0.02223846],"study_design_scores_gemma":[0.0002573052,0.0000906167,0.003775615,0.0003165353,0.00004575875,0.00002594053,0.0001482974,0.9529012,0.04057327,0.001593387,0.00003182826,0.0002402267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.42682,0.00002225789,0.5115688,0.00001321258,0.00004754955,0.0002886774,8.285775e-7,0.0009139611,0.06032475],"genre_scores_gemma":[0.7281096,7.651626e-7,0.2716672,0.0001084559,0.00006371055,0.000001512603,0.000001196841,0.00003657744,0.00001104664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3012896,"threshold_uncertainty_score":0.5609281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03417062721301328,"score_gpt":0.2001462983228269,"score_spread":0.1659756711098136,"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."}}