{"id":"W2948999504","doi":"10.1109/tmc.2019.2920819","title":"Optimal Mobile Computation Offloading with Hard Deadline Constraints","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computation offloading; Markov process; Markov chain; Distributed computing; Wireless; Markov decision process; Computation; Task (project management); Energy consumption; Channel (broadcasting); Mobile device; Real-time computing; Algorithm; Cloud computing; Computer network; Edge computing","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":[0.0003633941,0.0008097532,0.001031374,0.0003418421,0.0004993529,0.0008846338,0.0006878948,0.0004844596,0.00145194],"category_scores_gemma":[0.001485457,0.0003807574,0.0003851072,0.0005702757,0.0005556327,0.0008897829,0.0006957926,0.0006195339,0.0001978195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000666166,"about_ca_system_score_gemma":0.001607269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004147506,"about_ca_topic_score_gemma":0.004765237,"domain_scores_codex":[0.9995592,0.00006851516,0.00001763092,0.00009370004,0.00008783453,0.0001730671],"domain_scores_gemma":[0.9993027,0.0004428812,0.00008577906,0.00005489394,0.00005588941,0.00005786935],"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":[0.0001834828,0.00006939829,0.0005101179,0.00007065335,0.00001373764,0.0001332723,0.00004719747,0.9501467,0.004427217,0.01067571,0.0008089982,0.03291346],"study_design_scores_gemma":[0.000009397903,0.00001899164,0.0001113128,0.000003197375,0.000002905726,0.00001687372,0.00001508283,0.99485,0.0009618348,0.003761773,0.0002451786,0.000003502001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1557414,0.0005825571,0.8357374,0.0002526916,0.00009729677,0.000103094,0.0001165834,0.0005000074,0.006868994],"genre_scores_gemma":[0.9462369,0.0002282512,0.05088187,0.00004592086,0.00003564919,0.00006131146,0.00007303198,0.00007152017,0.002365583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004147506,"threshold_uncertainty_score":0.00824672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242198968725684,"score_gpt":0.2450203142305691,"score_spread":0.2325983245433122,"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."}}