{"id":"W2807716835","doi":"10.1109/icaset.2018.8376831","title":"Review — Challenges of mobility aware MAC protocols in WSN","year":2018,"lang":"en","type":"article","venue":"2018 Advances in Science and Engineering Technology International Conferences (ASET)","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer network; Computer science; Wireless sensor network; Network packet; Energy consumption; Throughput; Protocol (science); Wireless; Telecommunications; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0008788921,0.0001521505,0.0002574062,0.0006931386,0.00004344421,0.00003363472,0.001882912,0.00009351059,0.000008728043],"category_scores_gemma":[0.0004499991,0.0001383767,0.00001870512,0.001309688,0.00097961,0.0007883043,0.0004433199,0.0002096717,0.000003584636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009002601,"about_ca_system_score_gemma":0.0001358744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001503931,"about_ca_topic_score_gemma":0.0001148696,"domain_scores_codex":[0.9983152,0.00001783217,0.0003915257,0.0005576221,0.0003923994,0.0003254414],"domain_scores_gemma":[0.9989501,0.00007673501,0.0001319766,0.0004545592,0.0003416176,0.00004502087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001698446,0.0002169612,0.01365711,0.0006310591,0.00001165681,0.00002034354,0.0003473261,0.008175331,0.001602941,0.8177978,0.00009067042,0.1574318],"study_design_scores_gemma":[0.001157495,0.0008727278,0.0155299,0.01306489,0.000005983269,0.00009041405,0.0003563247,0.8800008,0.01597066,0.01786349,0.0540325,0.001054803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4429678,0.1333711,0.3360428,0.0342141,0.008055586,0.01778406,0.00007402129,0.002358839,0.02513179],"genre_scores_gemma":[0.9826105,0.007915305,0.008807028,0.0000727566,0.00002999976,0.0005548737,7.608991e-7,0.000004384525,0.000004437185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8718255,"threshold_uncertainty_score":0.5642839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987127606154094,"score_gpt":0.310585916040866,"score_spread":0.2907146399793251,"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."}}