{"id":"W1989497967","doi":"10.1109/tie.2012.2208439","title":"A Distributed TDMA Scheduling Algorithm for Target Tracking in Ultrasonic Sensor Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Time division multiple access; Wireless sensor network; Computer science; Scheduling (production processes); Network topology; Algorithm; Distributed algorithm; Job shop scheduling; Schedule; Node (physics); Ultrasonic sensor; Distributed computing; Mathematical optimization; Mathematics; Computer network; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008557658,0.0003995236,0.0004363665,0.000292329,0.0003745646,0.0001758211,0.0006629635,0.0006144693,0.00001256897],"category_scores_gemma":[0.0000330018,0.0004336385,0.0002516228,0.001519246,0.00005507796,0.000635593,0.000004264668,0.001602483,0.000007943111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006889941,"about_ca_system_score_gemma":0.0002545705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002558224,"about_ca_topic_score_gemma":0.00003704691,"domain_scores_codex":[0.9964027,0.0002123539,0.0006184586,0.0006062451,0.0003750605,0.001785163],"domain_scores_gemma":[0.9981562,0.0007261378,0.0001936671,0.000553593,0.0001077715,0.0002626544],"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":[0.00004462948,0.0003496144,0.00002253543,0.000002452896,0.00004668989,0.000002465195,0.00008148277,0.8754753,0.0001745939,0.0007086743,0.00004821695,0.1230433],"study_design_scores_gemma":[0.002096875,0.0002581708,0.00001092479,0.0000622657,0.00003245662,0.00002609725,0.00003556386,0.9780222,0.01644939,0.00004310137,0.002487969,0.0004749549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01172171,0.0004564489,0.983936,0.0002422761,0.002633351,0.0006341336,0.00003792823,0.0003159827,0.00002218004],"genre_scores_gemma":[0.9290307,0.0001156754,0.06964307,0.0001145566,0.0007906939,0.0001566441,0.00003065552,0.00006042755,0.00005764906],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9173089,"threshold_uncertainty_score":0.9998115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02678862577663535,"score_gpt":0.2569941601963598,"score_spread":0.2302055344197244,"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."}}