{"id":"W4206679948","doi":"10.18280/i2m.200602","title":"Adaptive Time Difference of Time of Arrival in Wireless Sensor Network Routing for Enhancing Quality of Service","year":2021,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computer network; Node (physics); Dependability; Real-time computing; Wireless sensor network; Network packet; Broadcasting (networking); Key distribution in wireless sensor networks; Transmission (telecommunications); Wireless; Energy consumption; Wireless network; Telecommunications; Engineering; Electrical engineering","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":[0.001133381,0.0001895568,0.0006310986,0.00009256315,0.0000496239,0.00001700932,0.0005049973,0.0001494865,0.00001125551],"category_scores_gemma":[0.0001967139,0.0001964184,0.0001216275,0.0008628076,0.00007862526,0.0001722935,0.0002134122,0.0001469456,0.000002168497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006676533,"about_ca_system_score_gemma":0.0001798229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001217799,"about_ca_topic_score_gemma":0.0002689382,"domain_scores_codex":[0.9971388,0.0005910416,0.001048847,0.0004479832,0.0004000635,0.0003732675],"domain_scores_gemma":[0.9967864,0.001316467,0.0008902508,0.0004597764,0.0005014592,0.00004569298],"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":[0.0002623183,0.0002456824,0.02254421,0.0001768981,0.0001053107,0.000005432275,0.00182942,0.5570138,0.3983705,0.009906012,0.00001353706,0.009526968],"study_design_scores_gemma":[0.001458532,0.0002145177,0.04563892,0.0002553001,0.00002285343,0.000003253222,0.0003420878,0.6665904,0.2842171,0.001010002,0.000001423331,0.0002456525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.76157,0.00003522467,0.2376641,0.0002090759,0.000154314,0.0001942408,0.00001690521,0.00003753933,0.0001185959],"genre_scores_gemma":[0.9436067,0.000008622456,0.05613733,0.0001211401,0.00004035305,0.00001371512,0.00002638363,0.00001162233,0.00003413443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1820367,"threshold_uncertainty_score":0.8009711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0332648598802494,"score_gpt":0.2880265016065715,"score_spread":0.254761641726322,"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."}}