{"id":"W2169158715","doi":"10.1007/s11036-011-0311-9","title":"Distributed Active Sensor Scheduling for Target Tracking in Ultrasonic Sensor Networks","year":2011,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Testbed; Wireless sensor network; Scalability; Real-time computing; Scheduling (production processes); Tracking (education); Key distribution in wireless sensor networks; Ultrasonic sensor; Mobile wireless sensor network; Computer network; Telecommunications; Wireless; Wireless network","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":[0.0002956837,0.0002914646,0.0003358827,0.0001024998,0.0003668152,0.0001349457,0.0005352708,0.0002401781,0.000008301593],"category_scores_gemma":[0.00001611259,0.0002970131,0.0001059504,0.000868739,0.0001106086,0.0002617194,0.0001307886,0.0003841757,0.00000277707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007061477,"about_ca_system_score_gemma":0.00002840072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002504479,"about_ca_topic_score_gemma":0.0000288929,"domain_scores_codex":[0.9978186,0.00007302847,0.0004474739,0.0007989856,0.0001345618,0.0007272953],"domain_scores_gemma":[0.9984006,0.0004623201,0.0002042335,0.0006166132,0.0001336112,0.0001826817],"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":[0.00001700767,0.0001531452,0.0008140087,0.000007872067,0.00001941979,0.00000261295,0.00009851801,0.9668524,0.00004619863,0.01048865,0.0000423499,0.02145777],"study_design_scores_gemma":[0.0004872089,0.00006131546,0.002635785,0.00004357614,0.00001548588,0.00001212374,0.0001601981,0.9912669,0.000283219,0.000318457,0.004358265,0.000357434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01848487,0.0007576115,0.9787332,0.00005585957,0.0001533131,0.001329614,0.0000269787,0.0002376732,0.0002209049],"genre_scores_gemma":[0.9505264,0.0002849998,0.04653703,0.0001240471,0.0003680985,0.001963213,0.0001255975,0.00003858989,0.00003199711],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9321961,"threshold_uncertainty_score":0.9999482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598549141332082,"score_gpt":0.2422723548787159,"score_spread":0.2262868634653951,"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."}}