{"id":"W4413344225","doi":"10.1109/tnse.2025.3600480","title":"Cost Minimization Resource Allocation with Service Instance Caching and Task Migration for UAV Mobile Edge Computing","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Computer science; Mobile edge computing; Resource allocation; Task (project management); Minification; Edge computing; Distributed computing; Enhanced Data Rates for GSM Evolution; Service (business); Resource management (computing); Mobile computing; Computer network; Resource (disambiguation); Mobile telephony; Server; Mobile radio; Telecommunications; Engineering; Business; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.0005026115,0.0001372395,0.0001192561,0.000199404,0.000824108,0.0002970217,0.0002341861,0.00004365794,6.59264e-8],"category_scores_gemma":[0.000008512849,0.0001347693,0.00001461199,0.001539643,0.00004916683,0.0005980592,0.000008436056,0.0001332928,3.17707e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007666359,"about_ca_system_score_gemma":0.0001024417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001795833,"about_ca_topic_score_gemma":0.0000241312,"domain_scores_codex":[0.9989185,0.00001302592,0.000173672,0.0003978564,0.0001770735,0.0003198549],"domain_scores_gemma":[0.9993744,0.0001689811,0.00004851824,0.0001859754,0.000152666,0.00006943484],"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":[0.000008304387,0.00001128055,0.00002916471,0.00004753864,0.000005934759,2.632904e-7,0.0009072582,0.9356304,0.0008842994,0.0002499881,0.000106364,0.06211915],"study_design_scores_gemma":[0.0002443779,0.00005502005,0.0003230583,0.0002279978,0.00001042635,0.00000599575,0.00005080659,0.9934574,0.001147651,0.00001747245,0.00431116,0.0001486546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06709451,0.0001054504,0.9307348,0.0003278021,0.001188121,0.000350255,3.098935e-7,0.0001400837,0.00005868858],"genre_scores_gemma":[0.957297,0.00002200552,0.04211655,0.0003055774,0.0001849586,0.00004147098,0.000001411782,0.00000871981,0.00002226177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8902025,"threshold_uncertainty_score":0.6338456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008862454812937463,"score_gpt":0.2191240592672707,"score_spread":0.2102616044543332,"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."}}