{"id":"W3137932838","doi":"10.1109/tccn.2021.3066619","title":"Delay-Aware and Energy-Efficient Computation Offloading in Mobile-Edge Computing Using Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computation offloading; Reinforcement learning; Server; Edge computing; Cloud computing; Mobile edge computing; Mobile device; Enhanced Data Rates for GSM Evolution; Edge device","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004187164,0.000219231,0.0002511772,0.0002751153,0.00144642,0.0002728198,0.0002721451,0.00008774703,0.000001244743],"category_scores_gemma":[0.000006060734,0.000260608,0.00006249452,0.0008562629,0.0001129542,0.0002107456,0.00009143942,0.0005127835,0.00000140655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000112031,"about_ca_system_score_gemma":0.00007036364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005163475,"about_ca_topic_score_gemma":0.00004584518,"domain_scores_codex":[0.9981881,0.0003669658,0.0004426251,0.0004521186,0.0001757256,0.0003744749],"domain_scores_gemma":[0.9981982,0.000972697,0.000163244,0.0003361159,0.0002308674,0.00009892761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007501851,0.00009726772,0.0002348505,0.00001979987,0.00003552432,0.00001140029,0.002356782,0.3448972,0.0001178329,0.0001068181,0.000001715938,0.6521133],"study_design_scores_gemma":[0.0005419016,0.00006637594,0.0001132336,0.0007105165,0.00003040303,0.00007364199,0.000631866,0.9968001,0.0003013224,0.00006352413,0.0003965431,0.0002705488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05658007,0.00312562,0.9385009,0.0000531105,0.001105232,0.0001295753,2.156278e-7,0.0001012906,0.0004039929],"genre_scores_gemma":[0.9883466,0.001566877,0.009728624,0.0001571146,0.0001309591,0.00001942454,0.00001079946,0.00001929267,0.0000203596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9317665,"threshold_uncertainty_score":0.9999846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03976490221543554,"score_gpt":0.2897704064381183,"score_spread":0.2500055042226828,"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."}}