{"id":"W4226329077","doi":"10.1109/jiot.2022.3163456","title":"An Adaptive Data Uploading Scheme for Mobile Crowdsensing via Deep Reinforcement Learning With Graph Neural Network","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Computer science; Upload; Server; Heuristic; Reinforcement learning; Edge computing; Heuristics; Mobile edge computing; Artificial intelligence; Machine learning; Enhanced Data Rates for GSM Evolution; Computer network","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":[0.000672865,0.0008967795,0.001077739,0.0004225844,0.000518319,0.0005556425,0.001721802,0.001102215,0.001532424],"category_scores_gemma":[0.002151191,0.0004069419,0.0005055806,0.0004182192,0.0008430717,0.001079005,0.001351297,0.001271623,0.0001940059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095875,"about_ca_system_score_gemma":0.001081489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076589,"about_ca_topic_score_gemma":0.01003992,"domain_scores_codex":[0.9995851,0.00008369896,0.00001902672,0.0001467491,0.00008162498,0.00008376636],"domain_scores_gemma":[0.9992324,0.0003900825,0.00009876153,0.00006415072,0.0001308532,0.00008368778],"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":[0.0001269519,0.0001158879,0.001044479,0.0000758327,0.00004092018,0.0001251577,0.0001082013,0.9253733,0.003738491,0.007202756,0.001847489,0.06020056],"study_design_scores_gemma":[0.000005382596,0.00001204823,0.00003729907,0.00000136877,0.000002715145,0.000004251524,0.000003736139,0.9987845,0.0001735911,0.0008584851,0.0001141692,0.0000024562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0489469,0.0003155243,0.9459422,0.0004450265,0.0001165803,0.00008805158,0.00006214522,0.0008487076,0.003234818],"genre_scores_gemma":[0.9478803,0.00009583549,0.04942343,0.0002197796,0.0000357623,0.0000957424,0.0000679919,0.0000385281,0.002142758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01076589,"threshold_uncertainty_score":0.02140641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02465674896718058,"score_gpt":0.2584645686220994,"score_spread":0.2338078196549189,"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."}}