{"id":"W2964098968","doi":"10.1109/tvt.2018.2890685","title":"Learning-Based Computation Offloading for IoT Devices With Energy Harvesting","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":545,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Mobile Communications Research Laboratory, Southeast University; National Natural Science Foundation of China","keywords":"Computation offloading; Computer science; Energy consumption; Mobile edge computing; Edge computing; Mobile device; Wireless; Edge device; Reinforcement learning; Computation; Latency (audio); Energy harvesting; Real-time computing; Server; Computer network; Internet of Things; Energy (signal processing); Embedded system; Cloud computing; Artificial intelligence; Engineering; Electrical engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001001094,0.0002465558,0.0002597455,0.000436004,0.0001749274,0.00003679027,0.000171859,0.0003263539,0.00001078919],"category_scores_gemma":[0.000006898797,0.000252099,0.00007262947,0.0005855572,0.00006944157,0.00007807768,9.417144e-7,0.0004621672,0.00001667467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009962335,"about_ca_system_score_gemma":0.00003422666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001932527,"about_ca_topic_score_gemma":0.0001516738,"domain_scores_codex":[0.9988971,0.00002822579,0.0002345583,0.000327594,0.0001407188,0.0003717456],"domain_scores_gemma":[0.9993334,0.0002292918,0.00006965573,0.0002280933,0.00009164178,0.00004796394],"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":[0.00001713143,0.00002644856,0.0001990664,0.00007647977,0.0000722785,0.000004580245,0.00001186248,0.9631532,0.005746423,0.0002489695,0.000007537128,0.030436],"study_design_scores_gemma":[0.0006734675,0.0003617738,0.00003252793,0.0002185362,0.00004125119,0.00001770619,0.00003804744,0.917846,0.0784578,0.00006316667,0.001959336,0.0002904451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2795973,0.00005618987,0.7181439,0.00008341004,0.0002444372,0.00014772,0.000002484097,0.001597925,0.0001265811],"genre_scores_gemma":[0.9785312,0.000005570238,0.02093122,0.00005238465,0.0000296393,0.0001627493,0.00001065842,0.0001006684,0.0001758627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6989339,"threshold_uncertainty_score":0.9999931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005511984357834534,"score_gpt":0.1938710994734164,"score_spread":0.1883591151155818,"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."}}