{"id":"W2751532433","doi":"10.1109/iscc.2017.8024601","title":"JLPR: Joint range-based localization using trilateration and packet routing in Wireless Sensor Networks with mobile sinks","year":2017,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Trilateration; Computer science; Wireless sensor network; Network packet; Global Positioning System; Real-time computing; Computer network; Geographic routing; Beacon; Triangular routing; Routing protocol; DSRFLOW; Source routing; Overhead (engineering); Node (physics); Dynamic Source Routing; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001361565,0.0001459723,0.0001792122,0.0001090777,0.0002203457,0.0002038638,0.00006958689,0.0001613653,0.00001115151],"category_scores_gemma":[0.0000213191,0.0001244842,0.00001691955,0.0001025931,0.00006804325,0.000229611,0.00002129429,0.0001229417,0.000001002334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006517728,"about_ca_system_score_gemma":0.00001148154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008044171,"about_ca_topic_score_gemma":0.0001706642,"domain_scores_codex":[0.9993119,0.00001791909,0.000211955,0.0001621568,0.00009073784,0.0002053396],"domain_scores_gemma":[0.9996032,0.00001744025,0.00006400117,0.0002416282,0.00004626078,0.00002745083],"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":[0.00001486983,0.000007542804,0.05323183,0.00003434796,0.00000639535,0.000007104273,0.00008917588,0.9424703,0.0006918547,0.0001846541,0.00001302276,0.00324888],"study_design_scores_gemma":[0.0007948622,0.00002837291,0.003606142,0.0001006636,0.00000926051,0.000004046452,0.0001508224,0.9769917,0.01810573,0.00001234334,0.00001665136,0.0001794581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4774669,0.0000224289,0.5218312,0.00001421563,0.00005671024,0.0001804328,0.000001072659,0.0002438528,0.0001831821],"genre_scores_gemma":[0.9976929,0.00002517942,0.002138859,0.00003569706,0.00003492888,0.00001491454,0.00001355185,0.00002975418,0.00001416572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5202261,"threshold_uncertainty_score":0.507632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470668448547967,"score_gpt":0.2193600169251984,"score_spread":0.2046533324397188,"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."}}