{"id":"W4403052626","doi":"10.1109/tmc.2024.3472643","title":"Secure Localization for Underwater Wireless Sensor Networks via AUV Cooperative Beamforming With Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Computer science; Reinforcement learning; Beamforming; Underwater; Wireless sensor network; Wireless; Computer network; Acoustic sensor; Underwater acoustic communication; Computer security; Telecommunications; Artificial intelligence; Acoustics; Oceanography; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006138471,0.0005798034,0.0005047671,0.0002049092,0.0003146686,0.0003618159,0.000557526,0.0005344755,0.0005689177],"category_scores_gemma":[0.001207413,0.000252945,0.0002940269,0.0002267937,0.0008257385,0.0006939175,0.001069069,0.0007720116,0.0001594725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000494521,"about_ca_system_score_gemma":0.0007290617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003967245,"about_ca_topic_score_gemma":0.00256693,"domain_scores_codex":[0.9996756,0.00009705185,0.00001448505,0.00006757939,0.0001038543,0.00004145933],"domain_scores_gemma":[0.9996413,0.000162079,0.00007158551,0.00003013393,0.00007230876,0.00002246949],"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":[0.0000422526,0.00002073839,0.0004619765,0.0000265244,0.00001412618,0.00004711856,0.00004821162,0.9591969,0.004789279,0.005125783,0.0003337928,0.02989329],"study_design_scores_gemma":[0.00000341992,0.0000167739,0.0000238491,0.000001228573,0.000001488216,0.000004179833,0.000003673251,0.9984024,0.0003780518,0.001071741,0.0000914607,0.000001738361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01999685,0.0001302037,0.9784569,0.0001499416,0.00002131169,0.00001564702,0.000007054454,0.0001802747,0.001042033],"genre_scores_gemma":[0.9490398,0.0001463096,0.04941535,0.000075646,0.00001658423,0.0000703956,0.000020213,0.00001684105,0.00119893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003967245,"threshold_uncertainty_score":0.007888317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009653512937284581,"score_gpt":0.2232774316319452,"score_spread":0.2136239186946606,"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."}}