{"id":"W4406207611","doi":"10.1109/jiot.2025.3527929","title":"Joint Task Partitioning and Resource Allocation in RAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Mobile edge computing; Computation offloading; Edge computing; Reinforcement learning; Markov decision process; Distributed computing; Server; Cloud computing; Computational resource; Resource allocation; Edge device; Mobile cloud computing; Enhanced Data Rates for GSM Evolution; Mobile computing; Computational complexity theory; Computer network; Artificial intelligence; Markov process","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":[0.0007776542,0.0001271321,0.0001908206,0.0002890069,0.000161408,0.0001628815,0.0003955125,0.00005064357,0.000003927505],"category_scores_gemma":[0.0001287156,0.000125454,0.00005584283,0.0003428962,0.00004296027,0.0003534452,0.0001274049,0.0005990609,0.000002588968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001433044,"about_ca_system_score_gemma":0.00004037378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001941171,"about_ca_topic_score_gemma":0.000002341076,"domain_scores_codex":[0.9985937,0.0001269233,0.0005416957,0.0002572739,0.0002478409,0.0002325602],"domain_scores_gemma":[0.9990387,0.0001935886,0.0003959038,0.0002024378,0.0001046576,0.00006472685],"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":[0.00002143082,0.0000295486,0.0005918242,0.00002169155,0.00001277255,0.000009419404,0.0009475679,0.978596,0.004271897,0.004245173,0.000281921,0.01097079],"study_design_scores_gemma":[0.0005233956,0.0001000305,0.000764699,0.0006789563,0.000006449057,0.00002168767,0.00004889431,0.9839373,0.01129418,0.001483263,0.001039279,0.0001018701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06768456,0.0000807071,0.929478,0.001373912,0.0001505137,0.0001326456,2.617259e-8,0.00004185884,0.00105778],"genre_scores_gemma":[0.9804033,0.00001212635,0.01838069,0.0009777113,0.00004285225,0.000006203565,0.000001443571,0.000007217278,0.0001684663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9127187,"threshold_uncertainty_score":0.5115865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0126047480095153,"score_gpt":0.2502499288195799,"score_spread":0.2376451808100646,"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."}}