{"id":"W3196318418","doi":"10.1109/tetci.2021.3102214","title":"Intelligent Resource Allocation and Task Offloading Model for IoT Applications in Fog Networks: A Game-Theoretic Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cloud computing; Server; Distributed computing; Energy consumption; Latency (audio); Edge computing; Task (project management); Node (physics); Resource allocation; Computational resource; Game theory; Computer network; Computational complexity theory","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":[],"consensus_categories":[],"category_scores_codex":[0.0004479514,0.0002076271,0.0002187163,0.0003441059,0.0002175321,0.0001528976,0.0004146752,0.0001044264,0.000001310344],"category_scores_gemma":[0.00002380998,0.0002424891,0.00008334032,0.0009314445,0.00007852841,0.0001847214,0.00001805642,0.0003591913,0.000002004307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001682531,"about_ca_system_score_gemma":0.0001467096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001234009,"about_ca_topic_score_gemma":0.00001686119,"domain_scores_codex":[0.9981043,0.00008888821,0.0005590448,0.000661422,0.0002410685,0.0003452392],"domain_scores_gemma":[0.9987485,0.000592839,0.00009581637,0.0003161566,0.0001674623,0.00007917602],"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":[0.000006805533,0.0001547365,0.00002231112,0.0000393607,0.000009922102,0.000001394238,0.00191621,0.8527347,0.000009163336,0.04555865,0.00001368455,0.09953309],"study_design_scores_gemma":[0.0001141108,0.0000236136,0.00004155635,0.00008000175,0.000007518142,0.000009753158,0.0001463347,0.9400835,0.0005064493,0.05842954,0.0003372206,0.0002204166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002482332,0.0002358602,0.9951807,0.0008174943,0.0006398435,0.0004285667,0.00000125359,0.00008099139,0.0001330124],"genre_scores_gemma":[0.7584202,0.00006811654,0.2408222,0.0002280803,0.0001594041,0.0001736868,0.00001345012,0.0000173922,0.00009745431],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7559378,"threshold_uncertainty_score":0.9888417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03644331204151008,"score_gpt":0.2946056680508555,"score_spread":0.2581623560093455,"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."}}