{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004689862,0.0004042672,0.0004372939,0.0003418561,0.0005436405,0.0003341271,0.0006760676,0.0001316733,0.00003067012],"category_scores_gemma":[0.000002995522,0.0003895969,0.0001720111,0.0008120447,0.0001211643,0.0005177711,0.00001794184,0.000592148,0.0003926721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001611027,"about_ca_system_score_gemma":0.0001524316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001729731,"about_ca_topic_score_gemma":0.000001083271,"domain_scores_codex":[0.9972001,0.0001232263,0.0005487665,0.0009087316,0.0004979775,0.0007212232],"domain_scores_gemma":[0.9983585,0.0004393878,0.0002304701,0.0005650349,0.0002154551,0.0001911718],"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":[0.00002330238,0.0001609732,0.0001519548,0.00003936891,0.00005457595,0.00001823238,0.0009359675,0.7813724,0.001495468,0.00003801356,0.0001094543,0.2156003],"study_design_scores_gemma":[0.001293424,0.0008881601,0.0002033684,0.000258592,0.00002442735,0.0001719606,0.0001715634,0.984331,0.01117908,0.00002204979,0.0008722264,0.0005842055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4054665,0.000022904,0.5892298,0.00002330082,0.003835022,0.0005376452,0.000001008424,0.0004508354,0.0004329348],"genre_scores_gemma":[0.8927506,0.000003072301,0.1064999,0.0001548208,0.0003634453,0.00003242961,0.000004337071,0.00004116571,0.0001503088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.487284,"threshold_uncertainty_score":0.9998556,"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."}}