{"id":"W4407949422","doi":"10.1109/cdc56724.2024.10886402","title":"A Distributed Reinforcement Learning Strategy to Maximize Coverage in a Hybrid Heterogeneous Sensor Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Wireless sensor network; Distributed computing; Heterogeneous network; Computer network; Artificial intelligence; Wireless network; Telecommunications; Wireless","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003435256,0.00027813,0.0002754,0.0001708374,0.0001081061,0.0006226927,0.0006130483,0.00007012428,0.00007679252],"category_scores_gemma":[0.00002810627,0.0002596962,0.0001055798,0.001173717,0.0000205141,0.0002194186,0.0004165756,0.0003721284,0.0001958656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002051754,"about_ca_system_score_gemma":0.00006954172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000958324,"about_ca_topic_score_gemma":0.00005176375,"domain_scores_codex":[0.9974308,0.0001320955,0.0004552457,0.0007502601,0.0003946358,0.0008369328],"domain_scores_gemma":[0.9989722,0.0002033098,0.00004600556,0.0005181864,0.00004534356,0.0002149167],"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":[0.00001482362,0.00002049815,0.00007368872,0.00001343222,0.00002157846,0.0009140791,0.00008254758,0.9773152,0.00007259577,0.009590785,0.00122827,0.0106525],"study_design_scores_gemma":[0.0002196261,0.0002105012,0.00006470327,0.0001188395,0.00000410266,0.00009667657,0.000009002984,0.9859016,0.0005304425,0.0001799584,0.01234061,0.0003239331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06458107,0.0002602201,0.930391,0.0003992121,0.0005643922,0.000272048,0.000002236482,0.0007388059,0.002791034],"genre_scores_gemma":[0.9910092,0.00004513868,0.006575888,0.0004240024,0.0001557076,0.00003469217,0.00003075174,0.00002792902,0.001696669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9264281,"threshold_uncertainty_score":0.9999855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151117361984713,"score_gpt":0.2303294367205222,"score_spread":0.2188182631006751,"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."}}