{"id":"W3108868139","doi":"10.18280/i2m.190502","title":"Software-Fault Mitigation for Derivation of Quality of Services (QoS) in Wireless Sensor Networks (WSN)","year":2020,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Islamic Azad University","keywords":"Wireless sensor network; Computer science; Fault tolerance; Quality of service; Fault (geology); Real-time computing; Throughput; Software; Fault detection and isolation; Task (project management); Key distribution in wireless sensor networks; Process (computing); Distributed computing; Reliability engineering; Embedded system; Wireless; Computer network; Wireless network; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.000640365,0.0001254609,0.0002841506,0.00008518376,0.00005395175,0.000033457,0.0003757545,0.000111781,0.00000110757],"category_scores_gemma":[0.0001302898,0.0001285406,0.00007549418,0.0004498202,0.00004053808,0.0004306315,0.00009054414,0.0000971527,0.000001064713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003522188,"about_ca_system_score_gemma":0.00005490219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006434441,"about_ca_topic_score_gemma":0.00001046692,"domain_scores_codex":[0.998386,0.0001867303,0.0006832944,0.0003009361,0.0002359338,0.0002071411],"domain_scores_gemma":[0.9987013,0.0003470716,0.0005285263,0.000182646,0.0001973868,0.00004310066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004953871,0.0002155121,0.6402637,0.001343417,0.0001089298,0.000002732717,0.008649079,0.04745445,0.05352472,0.005837211,0.000590192,0.2415147],"study_design_scores_gemma":[0.002683307,0.0003987129,0.3451612,0.00009365546,0.0000189074,0.000001228272,0.0002919767,0.5905583,0.05791866,0.002423164,0.0001341468,0.0003167469],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5616212,0.00001570342,0.4363033,0.0008890537,0.0008891281,0.0002113239,0.000001539102,0.00005463777,0.00001414006],"genre_scores_gemma":[0.9334919,0.000006897048,0.06550162,0.0005806747,0.0003461262,0.00001677399,0.00004766929,0.000007013422,0.000001371574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5431038,"threshold_uncertainty_score":0.5241735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04098414480103171,"score_gpt":0.3113867543068738,"score_spread":0.2704026095058422,"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."}}