{"id":"W4386025640","doi":"10.1109/tnse.2023.3306202","title":"To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Age of Information Optimization","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Agencia Estatal de Investigación; Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Ministero dell’Istruzione, dell’Università e della Ricerca; European Commission; Dipartimenti di Eccellenza","keywords":"Computer science; Wireless sensor network; Computation; Edge computing; Signal processing; Enhanced Data Rates for GSM Evolution; Real-time computing; Artificial intelligence; Algorithm; Computer hardware; Computer network; Digital signal processing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001349017,0.0007735224,0.0007557425,0.0002887105,0.0005860034,0.001509154,0.001479972,0.001526293,0.001956037],"category_scores_gemma":[0.004655525,0.0004558192,0.0004316689,0.0006449331,0.001751716,0.002668911,0.001285219,0.001323493,0.0001883592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125493,"about_ca_system_score_gemma":0.001035462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006642872,"about_ca_topic_score_gemma":0.005520362,"domain_scores_codex":[0.9991086,0.0003233634,0.00002959522,0.000215569,0.0001515957,0.0001712692],"domain_scores_gemma":[0.9980572,0.001374129,0.0001752617,0.0001324667,0.0001526994,0.0001083363],"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":[0.0001302678,0.000047439,0.0005986438,0.00007296967,0.00002396448,0.0001769449,0.00008453578,0.8579116,0.00130111,0.1261178,0.00131061,0.01222407],"study_design_scores_gemma":[0.000008081795,0.00001778069,0.0001017943,0.000005304378,0.000005822926,0.00001601362,0.0000236944,0.9753981,0.0002545103,0.02360793,0.0005537744,0.000007257378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05614576,0.0010371,0.9315742,0.002420662,0.0001325404,0.00006200037,0.0001120304,0.0001664678,0.00834915],"genre_scores_gemma":[0.9354886,0.001002538,0.05840163,0.0003247607,0.0001409549,0.00008632864,0.00005227206,0.00005974338,0.004443102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006642872,"threshold_uncertainty_score":0.01320845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01836815333925172,"score_gpt":0.2298125842803477,"score_spread":0.211444430941096,"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."}}