{"id":"W4213117259","doi":"10.1109/access.2022.3152787","title":"A Survey on Mobile Edge Computing Infrastructure: Design, Resource Management, and Optimization Approaches","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"University of Ottawa","keywords":"Computer science; Resource management (computing); Edge computing; Mobile computing; Mobile edge computing; Enhanced Data Rates for GSM Evolution; Distributed computing; Computer network; Telecommunications","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":[0.0009691669,0.00137242,0.0009076794,0.002458961,0.0004730485,0.002163748,0.001459654,0.001139282,0.003652721],"category_scores_gemma":[0.001635256,0.0008301286,0.0007486887,0.005435781,0.0004236684,0.003162165,0.0007628666,0.001244411,0.001489219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009046139,"about_ca_system_score_gemma":0.001353615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001377676,"about_ca_topic_score_gemma":0.001894992,"domain_scores_codex":[0.9992744,0.0001338284,0.00008397088,0.0001236011,0.0003028424,0.00008136696],"domain_scores_gemma":[0.9991067,0.0005017701,0.00007271206,0.00006712649,0.0002094761,0.00004223098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006611288,0.0001827233,0.001938883,0.00835795,0.0000854986,0.0002247884,0.0002278532,0.02441014,0.004967424,0.08339985,0.0287828,0.8473559],"study_design_scores_gemma":[0.00002431492,0.0003717676,0.002237577,0.004329027,0.0001661601,0.001353749,0.0005914308,0.0709718,0.00496971,0.05577776,0.8590952,0.0001114539],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.008225083,0.8194417,0.1275365,0.002849064,0.0008453086,0.0001742996,0.0003268691,0.000375369,0.0402259],"genre_scores_gemma":[0.04067047,0.8810973,0.0689531,0.0008062006,0.0009415135,0.0001580317,0.0005422081,0.0001295805,0.006701624],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003652721,"threshold_uncertainty_score":0.01221955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07463531463241692,"score_gpt":0.2737166129141081,"score_spread":0.1990812982816912,"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."}}