{"id":"W4405519226","doi":"10.2478/cait-2024-0036","title":"A Cost-Benefit Model for Feasible IoT Edge Resources Scalability to Improve Real-Time Processing Performance","year":2024,"lang":"en","type":"article","venue":"Cybernetics and Information Technologies","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Seneca Polytechnic","funders":"","keywords":"Computer science; Scalability; Enhanced Data Rates for GSM Evolution; Internet of Things; Distributed computing; Real-time computing; Embedded system; Artificial intelligence; Database","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":[0.001951553,0.001289327,0.0008626378,0.001222481,0.0007487747,0.002260003,0.002148173,0.0031973,0.009999805],"category_scores_gemma":[0.005281913,0.0007987794,0.001076068,0.00116567,0.00100363,0.002782594,0.0009992889,0.002229341,0.0005629653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004631898,"about_ca_system_score_gemma":0.001801032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01396651,"about_ca_topic_score_gemma":0.008391457,"domain_scores_codex":[0.9991955,0.0003287926,0.00002042112,0.00009243867,0.000133448,0.0002294983],"domain_scores_gemma":[0.9972327,0.001990413,0.0001693757,0.00007222885,0.0003586723,0.0001765988],"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":[0.00004905861,0.00004836804,0.0003750649,0.00003617148,0.00001191213,0.00009518416,0.0000280031,0.9719296,0.0004765815,0.02376259,0.0009334589,0.002253921],"study_design_scores_gemma":[0.00001031745,0.00002053956,0.0001860994,0.00001028686,0.000009860367,0.00002255505,0.00002563179,0.9936866,0.00008316596,0.005472034,0.0004650094,0.000007900595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2054506,0.001445892,0.7017723,0.005760091,0.0002510718,0.0007105136,0.001566206,0.0003600137,0.08268328],"genre_scores_gemma":[0.9663219,0.0004550175,0.02022297,0.0002265983,0.00003821802,0.0003085266,0.0001742004,0.00005306461,0.01219945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01396651,"threshold_uncertainty_score":0.03360689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01756012922285051,"score_gpt":0.2489134301608249,"score_spread":0.2313533009379744,"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."}}