{"id":"W3044716756","doi":"10.1109/lcomm.2020.3010324","title":"Optimal Resource Allocation for Wireless Powered Sensors: A Perspective From Age of Information","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Age of Information Optimization","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Energy harvesting; Computer science; Wireless sensor network; Bandwidth (computing); Base station; Wireless; Resource allocation; Transmitter power output; Real-time computing; Energy (signal processing); Computer network; Mathematical optimization; Telecommunications; Channel (broadcasting); Mathematics","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":[0.0001796309,0.0001298742,0.0001699145,0.0001412007,0.0001871318,0.0001511431,0.001807213,0.00006411618,0.000004482808],"category_scores_gemma":[0.0001803291,0.0001472895,0.00008438633,0.0004691347,0.0001180133,0.002136249,0.0002243798,0.0001411278,0.00003262164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00011741,"about_ca_system_score_gemma":0.00005801115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006974628,"about_ca_topic_score_gemma":0.000003348611,"domain_scores_codex":[0.9988446,0.0001095232,0.0005111118,0.0001553621,0.0002339888,0.0001453768],"domain_scores_gemma":[0.9975729,0.0002782799,0.0004327889,0.001258161,0.0003827139,0.00007513163],"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":[0.000255786,0.0002536957,0.00009592208,0.000134867,0.0003528361,0.000001877136,0.2514172,0.4421498,0.06877553,0.1827904,0.03791,0.01586213],"study_design_scores_gemma":[0.0008535248,0.00006899486,0.0002118532,0.00003562977,0.00002046674,0.000001572254,0.002829768,0.9753169,0.006153616,0.0001653543,0.01409037,0.0002518935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01419646,0.00002667568,0.9339526,0.05015777,0.00005542876,0.0005021422,0.00004831404,0.0001719373,0.0008886201],"genre_scores_gemma":[0.5629781,0.00002283805,0.431529,0.004931066,0.00003448411,0.00007072888,0.0004193533,0.000009442101,0.000004987217],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5487816,"threshold_uncertainty_score":0.6006292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0244651112847504,"score_gpt":0.2523668865431249,"score_spread":0.2279017752583745,"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."}}