{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005748462,0.0008379723,0.0007823604,0.0002806136,0.00036599,0.0008234835,0.0009534518,0.0007330648,0.00150588],"category_scores_gemma":[0.001373238,0.0003069268,0.0005177055,0.0003918792,0.0004932663,0.0005875021,0.0007036106,0.0008374091,0.0001878153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009185948,"about_ca_system_score_gemma":0.001497775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009765088,"about_ca_topic_score_gemma":0.008146799,"domain_scores_codex":[0.9996549,0.0001019144,0.00001627791,0.00007309112,0.00006466985,0.00008901768],"domain_scores_gemma":[0.9995413,0.0002463477,0.0000643835,0.00003375696,0.00006488513,0.00004938451],"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":[0.00006171974,0.00002528298,0.0003764133,0.00003531024,0.00001414757,0.00005153151,0.00001978077,0.9849855,0.00112339,0.002488681,0.000582526,0.01023569],"study_design_scores_gemma":[0.000003422581,0.000007412259,0.00003372472,0.00000165672,0.000002340196,0.000003659288,0.000003132221,0.9993182,0.0001414416,0.0003953912,0.00008835113,0.00000115192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05054779,0.0007175624,0.9433986,0.0004625638,0.00008942962,0.00006404439,0.0000577714,0.0003999478,0.004262234],"genre_scores_gemma":[0.9321944,0.0002312035,0.06522802,0.0001352723,0.00003002894,0.00009923023,0.0000868295,0.00006572079,0.001929223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009765088,"threshold_uncertainty_score":0.01941651,"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."}}