{"id":"W3192930814","doi":"10.1109/icc42927.2021.9500741","title":"Security Aware Cluster Head Selection with Coverage and Energy Optimization in WSNs for IoT","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Wireless sensor network; Selection (genetic algorithm); Internet of Things; Node (physics); Efficient energy use; Computer network; Energy (signal processing); Key (lock); Distributed computing; Computer security; Engineering; 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":[0.0006415723,0.0004279422,0.0004397566,0.0005245921,0.0004930997,0.000453413,0.000719865,0.0003323215,0.0005603494],"category_scores_gemma":[0.001265494,0.0002136933,0.0002997183,0.0007659529,0.0004433437,0.0006522957,0.0005768154,0.000361265,0.0001389269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006451609,"about_ca_system_score_gemma":0.0008041415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001770103,"about_ca_topic_score_gemma":0.002745513,"domain_scores_codex":[0.9995913,0.0001418208,0.00001649655,0.00007471313,0.0001299274,0.00004586799],"domain_scores_gemma":[0.9996146,0.0001900694,0.0000665907,0.0000411476,0.00006434623,0.00002325054],"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":[0.0002062888,0.00008331991,0.00208499,0.00009436972,0.00006274232,0.00009146078,0.0001707241,0.848703,0.01566196,0.01114837,0.001965316,0.1197275],"study_design_scores_gemma":[0.00001098904,0.00006089666,0.0005789631,0.000004867031,0.00001457703,0.00004751347,0.00003698726,0.9920411,0.00328948,0.003034695,0.0008716719,0.000008239265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04989972,0.0006858684,0.9464954,0.0002468573,0.00005115375,0.00008256769,0.00003270488,0.0002995257,0.002206182],"genre_scores_gemma":[0.883755,0.00051678,0.1129432,0.00008342538,0.00005386173,0.0001005594,0.00008872713,0.00005014981,0.002408204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001770103,"threshold_uncertainty_score":0.004680991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006385597109349584,"score_gpt":0.2139250760601875,"score_spread":0.207539478950838,"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."}}