{"id":"W2143206770","doi":"10.1109/itng.2007.97","title":"Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Wireless sensor network; Computer science; Cluster analysis; Routing (electronic design automation); Computer network; Genetic algorithm; Key distribution in wireless sensor networks; Cluster (spacecraft); Energy (signal processing); Routing protocol; Wireless; Efficient energy use; Hierarchical routing; Distributed computing; Routing algorithm; Wireless Routing Protocol; Wireless network; Engineering; Telecommunications; Artificial intelligence; Electrical engineering; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007163396,0.0003285167,0.0003414539,0.0003596185,0.0001415505,0.0001298774,0.001010074,0.0002357075,0.000006243109],"category_scores_gemma":[0.00001183836,0.0003149215,0.0001503418,0.00106361,0.00008230464,0.00009469174,0.0002775183,0.0001885773,0.000006154808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001729852,"about_ca_system_score_gemma":0.00004221038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001349413,"about_ca_topic_score_gemma":0.0002658868,"domain_scores_codex":[0.9968001,0.0000793717,0.0006373069,0.00085655,0.0004024135,0.001224215],"domain_scores_gemma":[0.9981349,0.0005838664,0.000142647,0.0007644508,0.0001280842,0.0002460943],"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":[0.000009142466,0.0001063081,0.0001008243,0.00000314273,0.000008650661,0.0000488798,0.00005182159,0.7374329,0.00004734122,0.009398131,0.0001916635,0.2526012],"study_design_scores_gemma":[0.0008668676,0.00007874367,0.0008974389,0.00002738851,0.000004717249,0.00002968014,0.00003904936,0.9954619,0.0008083068,0.00003152322,0.001345445,0.0004089066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02689426,0.0001692627,0.9696432,0.000137333,0.001225789,0.0002723468,9.950564e-7,0.0002927036,0.001364102],"genre_scores_gemma":[0.5786558,0.00002642918,0.4195476,0.0008338545,0.0003032631,0.00003127121,0.000005274833,0.00003872659,0.0005577528],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5517615,"threshold_uncertainty_score":0.9999303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007266053485058463,"score_gpt":0.2187897343482674,"score_spread":0.211523680863209,"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."}}