{"id":"W1965155889","doi":"10.1109/giis.2014.6934271","title":"Energy efficient clustering protocol for WSN using PSO","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Energy consumption; Wireless sensor network; Cluster analysis; Network packet; Scalability; Efficient energy use; Routing protocol; Computer network; Protocol (science); Throughput; Distributed computing; Cluster (spacecraft); Network topology; Real-time computing; Engineering; Wireless; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002628769,0.0002943155,0.0003299803,0.0003445936,0.0003846309,0.0003876173,0.0004120317,0.0004338926,0.000816071],"category_scores_gemma":[0.0006216689,0.0001093531,0.0003191613,0.0006164578,0.0002353305,0.0004420659,0.000499169,0.0005503807,0.0002925397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003126671,"about_ca_system_score_gemma":0.0004755961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00140203,"about_ca_topic_score_gemma":0.001439142,"domain_scores_codex":[0.9997757,0.00005640533,0.00001716308,0.00003462435,0.00009970761,0.00001638489],"domain_scores_gemma":[0.9999013,0.00002344098,0.00001373418,0.00001380388,0.00004129847,0.000006532491],"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":[0.0001347066,0.0001274498,0.0009688882,0.0005385036,0.0001547337,0.0003049183,0.0003291734,0.4390269,0.07320955,0.0977616,0.01473922,0.3727043],"study_design_scores_gemma":[0.0000466915,0.0001863673,0.000732519,0.00002765757,0.00002544848,0.0002760841,0.00005025708,0.9437171,0.009007364,0.0117502,0.03414436,0.00003593483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01213468,0.0009581788,0.975803,0.000374565,0.0001742193,0.0002111309,0.00007637099,0.0005711058,0.009696732],"genre_scores_gemma":[0.4358385,0.001481155,0.5496362,0.0002307581,0.00006412545,0.0007875738,0.000371417,0.0001123638,0.01147794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00140203,"threshold_uncertainty_score":0.002787709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02896747357686941,"score_gpt":0.2860958947175747,"score_spread":0.2571284211407053,"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."}}