{"id":"W1524725909","doi":"10.1109/icc.2015.7248815","title":"Energy efficient offloading for competing users on a shared communication channel","year":2015,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Nash equilibrium; Upload; Cloud computing; Energy consumption; Channel (broadcasting); Base station; Server; Computation offloading; Game theory; Computer network; Distributed computing; Mathematical optimization; Edge computing; Operating system","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":[0.001074595,0.001042887,0.001418683,0.0004467962,0.00117164,0.001743979,0.001314327,0.001296892,0.002439659],"category_scores_gemma":[0.002596538,0.0004502016,0.0007068346,0.0006010632,0.001436403,0.001670998,0.001683858,0.0007407188,0.000259448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273579,"about_ca_system_score_gemma":0.001514122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008079425,"about_ca_topic_score_gemma":0.008564791,"domain_scores_codex":[0.9989058,0.0003294341,0.00002629466,0.0001654508,0.0001954628,0.0003776455],"domain_scores_gemma":[0.9981944,0.001207541,0.0001381327,0.0001355218,0.0001745395,0.0001498943],"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":[0.0001285715,0.0000969812,0.0006564158,0.00004106302,0.00003804048,0.0003114897,0.00008524991,0.9681932,0.003856916,0.01727438,0.0002885181,0.009029124],"study_design_scores_gemma":[0.000009575531,0.00003087572,0.0001175645,0.000002118661,0.000005759195,0.00003182021,0.00004479826,0.9943964,0.0005341971,0.004625042,0.0001951808,0.000006671754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3523549,0.0002471011,0.6339784,0.0002741511,0.00006602631,0.0002079851,0.00007232302,0.0001266505,0.01267245],"genre_scores_gemma":[0.9759104,0.00007700695,0.02009574,0.00004504001,0.00001950749,0.00005992819,0.0000243009,0.00002530612,0.003742643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008079425,"threshold_uncertainty_score":0.01606482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06050019704322974,"score_gpt":0.26795525963819,"score_spread":0.2074550625949603,"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."}}