{"id":"W4390344873","doi":"10.18280/jesa.560611","title":"Efficient Geographic Routing for High-Speed Data in Wireless Multimedia Sensor Networks","year":2023,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Vision Group on Science and Technology; Visvesvaraya Technological University","keywords":"Geographic routing; Computer science; Computer network; Routing (electronic design automation); Wireless sensor network; Dynamic Source Routing; Multimedia; Routing protocol","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003715595,0.0004331415,0.000308969,0.0008656568,0.0004477311,0.0004960551,0.0006345145,0.0003740416,0.000611248],"category_scores_gemma":[0.000883122,0.0001325189,0.0003070875,0.0009030036,0.0002400885,0.0009523827,0.0004421887,0.0002087559,0.0001537795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005036247,"about_ca_system_score_gemma":0.0005848954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002016899,"about_ca_topic_score_gemma":0.004560279,"domain_scores_codex":[0.9997328,0.00008471344,0.00001651474,0.00004256614,0.0001019767,0.00002134676],"domain_scores_gemma":[0.9997624,0.00007059825,0.0000449645,0.00003975603,0.0000724379,0.00000989521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001972859,0.00006553699,0.003249285,0.0006427113,0.0001443255,0.000372381,0.0003101935,0.2963857,0.06212296,0.0264946,0.006479203,0.6035358],"study_design_scores_gemma":[0.00003180591,0.0002164692,0.002189413,0.00003235948,0.00006298904,0.0004792253,0.0002770128,0.9372346,0.02548816,0.01495061,0.01899493,0.00004242838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05821338,0.002914247,0.9336938,0.0004882286,0.0001744672,0.0001146252,0.0001349261,0.0009949704,0.003271465],"genre_scores_gemma":[0.7457595,0.001955904,0.2489002,0.0001271338,0.00006916043,0.0001269262,0.000335798,0.00005827809,0.002667136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002016899,"threshold_uncertainty_score":0.00401032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03799234531466469,"score_gpt":0.2732673437847348,"score_spread":0.2352749984700701,"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."}}