{"id":"W4379163433","doi":"10.18280/i2m.220203","title":"An Energy Efficient Technique for Improved Network Lifetime in Wireless Sensor Network (WSN) Through Energy, Distance, and Density-Based Clustering","year":2023,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wireless sensor network; Cluster analysis; Computer science; Computer network; Energy (signal processing); Key distribution in wireless sensor networks; Wireless network; Wireless; Telecommunications; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000465684,0.0005159692,0.0004470226,0.00107576,0.0009075138,0.0004986518,0.001024627,0.0005261595,0.0006587458],"category_scores_gemma":[0.0009828223,0.0001962897,0.0005700692,0.001260996,0.0003415566,0.001333342,0.0006908808,0.0005040005,0.000260735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007730434,"about_ca_system_score_gemma":0.0007557667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002187056,"about_ca_topic_score_gemma":0.003338187,"domain_scores_codex":[0.9996704,0.00006035597,0.00001690071,0.00006752673,0.0001522981,0.00003246639],"domain_scores_gemma":[0.9996663,0.00009348006,0.00004504241,0.0000518524,0.0001255925,0.00001766964],"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":[0.0001692236,0.000232653,0.002007209,0.0005339807,0.0001333113,0.0002745501,0.0006132002,0.4452139,0.07853265,0.03792565,0.006032838,0.4283308],"study_design_scores_gemma":[0.00002351443,0.0004079689,0.001628468,0.00005688058,0.00009372667,0.0007102317,0.0002410722,0.9224333,0.04148231,0.01208501,0.02075857,0.00007901916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04314974,0.002564365,0.9467512,0.0004722825,0.0001381691,0.0001220545,0.00007159153,0.0009784213,0.005752125],"genre_scores_gemma":[0.7248568,0.002633128,0.2657939,0.000213269,0.00008094322,0.0001770075,0.0001734491,0.0001401364,0.005931423],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002187056,"threshold_uncertainty_score":0.005608857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409019871638017,"score_gpt":0.263728644769947,"score_spread":0.2496384460535668,"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."}}