{"id":"W3140719572","doi":"10.1109/tnet.2021.3066558","title":"Multi-Persona Mobility: Joint Cost-Effective and Resource-Aware Mobile-Edge Computation Offloading","year":2021,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; École de technologie supérieure; Lebanese American University","keywords":"Computer science; Computation offloading; Mobile device; Mobile computing; Mobile edge computing; Context (archaeology); Edge computing; Computer network; Distributed computing; Enhanced Data Rates for GSM Evolution; Server; Operating system; 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":[0.0004442377,0.0007720197,0.0008996585,0.0003730312,0.000502297,0.0008644283,0.001087553,0.0008060425,0.001514155],"category_scores_gemma":[0.0008894213,0.0003066729,0.0006865856,0.0004521064,0.0003835054,0.0008519666,0.001189582,0.000665741,0.0002184107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000539717,"about_ca_system_score_gemma":0.001019221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004496403,"about_ca_topic_score_gemma":0.004496059,"domain_scores_codex":[0.999648,0.00007846034,0.00001249115,0.00006193684,0.00007788472,0.0001211528],"domain_scores_gemma":[0.9997802,0.00007657828,0.00003493423,0.00003125544,0.00003502947,0.00004192259],"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":[0.00007914974,0.00007843051,0.00082737,0.00004042998,0.00003570346,0.0001272408,0.0000490668,0.9384336,0.003728225,0.006048451,0.001082438,0.04947002],"study_design_scores_gemma":[0.000003469758,0.00002140035,0.0001022058,0.000002343068,0.000005719409,0.00001887482,0.00001220982,0.9979259,0.0003064523,0.001254738,0.0003433675,0.000003331127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04861461,0.0003375048,0.9448836,0.0003606198,0.00008509245,0.00005255238,0.00003875118,0.0004025767,0.005224598],"genre_scores_gemma":[0.929847,0.0001482584,0.06716762,0.00009615635,0.00002982745,0.00005871253,0.00004725523,0.0000455019,0.002559665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004496403,"threshold_uncertainty_score":0.008940458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04643610454694119,"score_gpt":0.2822271360410389,"score_spread":0.2357910314940977,"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."}}