{"id":"W4409020031","doi":"10.1109/tifs.2025.3550812","title":"Blockchain-Enabled Computing Offloading and Resource Allocation in Multi-UAVs MEC Network: A Stackelberg Game Learning Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Science Foundation of Hunan Province; Natural Sciences and Engineering Research Council of Canada; Key Laboratory of Intelligent Multimedia Technology; National Natural Science Foundation of China","keywords":"Stackelberg competition; Computer science; Resource allocation; Computer network; Resource management (computing); Server; Game based learning; Distributed computing; Multimedia","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007415375,0.0001832281,0.0002191502,0.0003372882,0.0005342041,0.0003180917,0.0001685672,0.0001238785,3.944923e-7],"category_scores_gemma":[0.00001548124,0.0001940186,0.00004595639,0.0006958605,0.00005537887,0.0005825015,0.00002138153,0.0005027509,0.000002095644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007462336,"about_ca_system_score_gemma":0.00005149929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008184932,"about_ca_topic_score_gemma":0.00001834466,"domain_scores_codex":[0.9986638,0.00009369533,0.0004725019,0.0002481116,0.0001762138,0.0003457186],"domain_scores_gemma":[0.9993401,0.0001500642,0.0001442406,0.0001875355,0.0001036812,0.00007433542],"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":[0.00005640492,0.0001054317,0.0005436867,0.0002843883,0.00006451891,0.000001734955,0.0351377,0.6644838,0.00002435084,0.01192625,0.0004370029,0.2869347],"study_design_scores_gemma":[0.0008101832,0.00003793288,0.0004633888,0.0001113325,0.000009982928,0.000008445138,0.0003731521,0.9945704,0.0001692434,0.0009168198,0.002343084,0.000186058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08748519,0.0000512262,0.9101692,0.0001613412,0.0007553176,0.0002388462,4.97837e-7,0.0001333603,0.001005008],"genre_scores_gemma":[0.9832171,0.00002298046,0.01633571,0.000296848,0.00006002677,0.000008408925,0.000007157729,0.000005677546,0.00004615527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8957319,"threshold_uncertainty_score":0.7911849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01147589717791176,"score_gpt":0.2278855410596237,"score_spread":0.216409643881712,"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."}}