{"id":"W4210698675","doi":"10.1109/lwc.2022.3146207","title":"Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing","year":2022,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Royal Academy of Engineering; Ho Chi Minh City University of Technology and Education","keywords":"Computer science; Server; Mobile edge computing; Latency (audio); Edge computing; Computation offloading; Cloud computing; Computer network; Distributed computing; Operating system","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":[0.0002182067,0.0005126191,0.0004189087,0.0002067958,0.0003935572,0.0006171786,0.0006257762,0.0003649529,0.0009730037],"category_scores_gemma":[0.0004248508,0.0001630289,0.0002447309,0.0004204706,0.0004479085,0.001097391,0.000629532,0.0004689124,0.0001317374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004151232,"about_ca_system_score_gemma":0.0004243206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001258345,"about_ca_topic_score_gemma":0.002101104,"domain_scores_codex":[0.9998274,0.00004258994,0.000006108805,0.00003793208,0.00003891473,0.00004713949],"domain_scores_gemma":[0.9998665,0.00005443888,0.00001474446,0.00001883812,0.00003028461,0.00001512726],"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":[0.000298064,0.0001229282,0.00139288,0.0001230315,0.00004264306,0.0004145267,0.0001377189,0.8230914,0.03558121,0.04453489,0.001492083,0.09276862],"study_design_scores_gemma":[0.000003445458,0.00004478111,0.00008146312,0.000002265019,0.000006099837,0.00004729499,0.00001592201,0.9952368,0.001613538,0.002451026,0.0004932309,0.000004156777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08628142,0.000586304,0.9084507,0.0001567004,0.00006913388,0.00003243495,0.00002359842,0.0001216072,0.004278183],"genre_scores_gemma":[0.9477043,0.0002657859,0.05008328,0.0000661404,0.00002397552,0.00001903756,0.00001632962,0.00001602172,0.001805245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001258345,"threshold_uncertainty_score":0.003255069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02229109243562341,"score_gpt":0.2535970482659557,"score_spread":0.2313059558303323,"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."}}