{"id":"W4315629526","doi":"10.1109/globecom48099.2022.10000950","title":"An IoT Traffic Modeling Framework and its Application to Autonomous Edge Scaling","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computer network; Enhanced Data Rates for GSM Evolution; Flexibility (engineering); Distributed computing; Scope (computer science); Internet of Things; Computer security; Telecommunications","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":[0.0008856509,0.00077474,0.0003629126,0.0007916167,0.0005770727,0.001060036,0.001124433,0.001049733,0.001113727],"category_scores_gemma":[0.001853704,0.0003346986,0.0008866591,0.0008567807,0.000443154,0.001024334,0.0008380282,0.0009360081,0.000320749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008194845,"about_ca_system_score_gemma":0.000871254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007016837,"about_ca_topic_score_gemma":0.003443729,"domain_scores_codex":[0.999702,0.0001028612,0.00001971773,0.00005320634,0.00008837428,0.00003383052],"domain_scores_gemma":[0.9995013,0.0001918379,0.00007295879,0.00004960036,0.0001460229,0.00003824176],"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":[0.00001875838,0.00005495071,0.0008046085,0.00002521117,0.00001487374,0.0001604322,0.00007839688,0.8599087,0.002331716,0.1214953,0.00150485,0.01360216],"study_design_scores_gemma":[6.813615e-7,0.000003867317,0.00003343357,0.000001853772,0.00000127861,0.00001589132,0.000004569071,0.9928524,0.0000810476,0.006482226,0.0005207251,0.00000206807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006393362,0.0001044168,0.990732,0.0001971467,0.00004104689,0.00003277485,0.00007836056,0.0002244529,0.002196492],"genre_scores_gemma":[0.6087857,0.001081035,0.3825565,0.0002264045,0.0002758066,0.000365653,0.0005534897,0.0002100692,0.005945399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007016837,"threshold_uncertainty_score":0.01395202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04158601914213526,"score_gpt":0.3086542745226381,"score_spread":0.2670682553805028,"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."}}