{"id":"W4366957259","doi":"10.1109/tcomm.2023.3269839","title":"Robust Task Offloading and Resource Allocation in Mobile Edge Computing With Uncertain Distribution of Computation Burden","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Mobile edge computing; Computer science; Computation offloading; Computation; Task (project management); Edge computing; Resource allocation; Distributed computing; Enhanced Data Rates for GSM Evolution; Resource management (computing); Mathematical optimization; Computer network; Algorithm; Engineering; Mathematics; 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":[0.0009739362,0.001156816,0.001413951,0.0003588508,0.0006646794,0.001355733,0.001262736,0.0009138763,0.001400748],"category_scores_gemma":[0.002673845,0.000563965,0.0005974282,0.0007827902,0.0008918462,0.001437905,0.001389947,0.001038018,0.0001975816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176788,"about_ca_system_score_gemma":0.001169611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00506566,"about_ca_topic_score_gemma":0.00385086,"domain_scores_codex":[0.9990562,0.0002126224,0.00004316576,0.0002589486,0.0001780755,0.0002510836],"domain_scores_gemma":[0.9987979,0.0007745873,0.0001395619,0.0000949036,0.0001235816,0.00006943428],"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":[0.0001321908,0.00003899713,0.0004181374,0.00008664334,0.00002587865,0.0001650605,0.00004951037,0.9692645,0.003066427,0.009368389,0.0008578068,0.01652642],"study_design_scores_gemma":[0.000003611508,0.00001350025,0.00007831819,0.000002886835,0.000003766439,0.00001848179,0.0000102077,0.9968182,0.000343837,0.002576898,0.0001263441,0.000003832273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03302281,0.0006299975,0.9632329,0.0003242298,0.00005442312,0.00004663258,0.00005925103,0.0001584122,0.002471385],"genre_scores_gemma":[0.9455019,0.0004271806,0.05190145,0.000128432,0.00008192207,0.00008159911,0.0000639451,0.0000623159,0.001751344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00506566,"threshold_uncertainty_score":0.01007235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04151013473356752,"score_gpt":0.2769400113834664,"score_spread":0.2354298766498989,"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."}}