{"id":"W2908395186","doi":"10.1007/978-3-030-02931-9_5","title":"An Obstacle-Aware Clustering Protocol for Wireless Sensor Networks with Irregular Terrain","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Cluster analysis; Non-line-of-sight propagation; Obstacle; Wireless sensor network; Energy consumption; Path (computing); Protocol (science); Network packet; Terrain; Real-time computing; Computer network; Packet loss; Path loss; Selection (genetic algorithm); Wireless; Distributed computing; Artificial intelligence; Telecommunications","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":[0.0003969161,0.0005701657,0.000664333,0.0007867912,0.0009964277,0.0005840459,0.002605369,0.0007236218,0.00138216],"category_scores_gemma":[0.00133575,0.0003564197,0.0004330127,0.001369293,0.000493021,0.001319862,0.002058714,0.0009553004,0.0005290158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005424724,"about_ca_system_score_gemma":0.0008069844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002681934,"about_ca_topic_score_gemma":0.004982152,"domain_scores_codex":[0.999694,0.00004394453,0.00002133417,0.00004486232,0.0001567098,0.00003919149],"domain_scores_gemma":[0.9994622,0.0001607421,0.00004467731,0.0001378889,0.0001557435,0.00003882602],"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":[0.0002802403,0.0001785828,0.0007150989,0.0003542671,0.0001265195,0.0002606886,0.000367573,0.4841082,0.03885961,0.03649076,0.01566797,0.4225905],"study_design_scores_gemma":[0.0000275793,0.0001053606,0.0005127765,0.00002430147,0.00004535733,0.0002551743,0.00009394959,0.9688873,0.009279503,0.01134471,0.009385771,0.00003823369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03063212,0.000976711,0.9598439,0.0003049487,0.0003565079,0.000225262,0.0001482777,0.001475178,0.006037017],"genre_scores_gemma":[0.4754679,0.001369219,0.508894,0.0002068605,0.0001451061,0.0004420737,0.000778455,0.0003031327,0.01239328],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002681934,"threshold_uncertainty_score":0.005332589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712781875990676,"score_gpt":0.263244985332622,"score_spread":0.2461171665727152,"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."}}