{"id":"W2766713695","doi":"10.1109/jiot.2018.2838022","title":"Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game","year":2018,"lang":"en","type":"preprint","venue":"IEEE Internet of Things Journal","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computation offloading; Computer science; Cloud computing; Nash equilibrium; Edge computing; Latency (audio); Distributed computing; Price of anarchy; Computation; Quality of experience; Fog computing; Mobile edge computing; Game theory; Computer network; Quality of service; Mathematical optimization; Algorithm; Operating system; Telecommunications","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":[0.000732518,0.000789131,0.0006977023,0.0002804751,0.0009558703,0.001258395,0.001122379,0.001347476,0.002165227],"category_scores_gemma":[0.001781012,0.0002864731,0.0006851028,0.0003764458,0.001288725,0.00166716,0.001240327,0.001170222,0.0001332491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001769734,"about_ca_system_score_gemma":0.001865517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007581602,"about_ca_topic_score_gemma":0.005568499,"domain_scores_codex":[0.9994168,0.0002360496,0.00001860163,0.00007231548,0.0001126208,0.0001436486],"domain_scores_gemma":[0.999418,0.0003579029,0.00004457328,0.00002655738,0.00005878363,0.00009423314],"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":[0.0002088223,0.0001730723,0.0009084576,0.0001365078,0.00005387124,0.0005801992,0.0002553165,0.6717814,0.006135079,0.3040917,0.003061571,0.01261399],"study_design_scores_gemma":[0.00002170435,0.00003386887,0.000153877,0.000006757487,0.000007692985,0.00003487649,0.00004981683,0.9656334,0.0002084223,0.03324398,0.0005963999,0.000009105942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1803652,0.0003518294,0.7767413,0.001883317,0.0001225066,0.0003767904,0.0001675118,0.0001106515,0.03988086],"genre_scores_gemma":[0.9682024,0.0002046137,0.02782327,0.0001923734,0.00003004739,0.0001468873,0.0000305226,0.0000137086,0.003356209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007581602,"threshold_uncertainty_score":0.01507491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03646654689644201,"score_gpt":0.3010352679509106,"score_spread":0.2645687210544685,"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."}}