{"id":"W4293061092","doi":"10.1186/s13677-022-00290-w","title":"Joint task offloading and resource allocation in mobile edge computing with energy harvesting","year":2022,"lang":"en","type":"article","venue":"Journal of Cloud Computing Advances Systems and Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; EnodeB; Mobile edge computing; Computation offloading; Energy consumption; Resource allocation; Telecommunications link; Server; Lyapunov optimization; Computer network; Real-time computing; Enhanced Data Rates for GSM Evolution; Distributed computing; Edge computing; Base station; User equipment; Engineering; Telecommunications","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":[0.0005607484,0.0008789389,0.001050373,0.0003566761,0.0005545124,0.0009527821,0.0009956519,0.0006835496,0.001967502],"category_scores_gemma":[0.0008330002,0.0003132997,0.0005277407,0.0007396925,0.0004339162,0.0008748227,0.0009469742,0.0005782088,0.0001881941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005583455,"about_ca_system_score_gemma":0.000970355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004569646,"about_ca_topic_score_gemma":0.004547379,"domain_scores_codex":[0.9995037,0.0001199497,0.0000249776,0.0001105815,0.00009279598,0.0001479474],"domain_scores_gemma":[0.9996462,0.0001894287,0.00003433368,0.00003294295,0.00005366767,0.00004346596],"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":[0.0002176269,0.000131995,0.0007702802,0.0001657912,0.00004523399,0.0001838398,0.00004834574,0.930183,0.006249617,0.004758007,0.002230311,0.055016],"study_design_scores_gemma":[0.000009377355,0.00002570649,0.0001267401,0.000004560684,0.000007238778,0.0000195576,0.00001642072,0.9971712,0.0007100271,0.001548934,0.0003561672,0.000004150606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1105986,0.001937417,0.8761817,0.0005390896,0.0001483141,0.0001419358,0.0001065,0.0004822879,0.00986409],"genre_scores_gemma":[0.9314401,0.0004162795,0.06493269,0.0001549063,0.00004944377,0.0001137544,0.00008186976,0.00005920615,0.002751864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004569646,"threshold_uncertainty_score":0.009086132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132594490785074,"score_gpt":0.23331654862432,"score_spread":0.2219906037164693,"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."}}