{"id":"W4205100419","doi":"10.1109/jiot.2022.3143539","title":"Distributed Offloading in Overlapping Areas of Mobile-Edge Computing for Internet of Things","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Beijing Municipality; Beijing Nova Program; Fundo para o Desenvolvimento das Ciências e da Tecnologia; Beijing Municipal Commission of Education; National Natural Science Foundation of China","keywords":"Computer science; Mobile edge computing; Server; Distributed computing; Computation offloading; Nash equilibrium; Enhanced Data Rates for GSM Evolution; Task (project management); Edge computing; Base station; Computer network; Mathematical optimization; Artificial intelligence","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":[0.0004587944,0.000520546,0.0005308188,0.00033075,0.000755874,0.0006134175,0.0006783757,0.0004201721,0.0006663908],"category_scores_gemma":[0.0008928236,0.0001966643,0.0004217178,0.0004959227,0.0004909068,0.001039669,0.0009567218,0.0004716539,0.0001019253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006660072,"about_ca_system_score_gemma":0.0006879052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002652378,"about_ca_topic_score_gemma":0.003960722,"domain_scores_codex":[0.9995915,0.000103947,0.00001570826,0.00009925524,0.00008868455,0.0001008764],"domain_scores_gemma":[0.9995986,0.0001977754,0.00005067926,0.00005325815,0.00005842829,0.00004138353],"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":[0.000252344,0.0001493686,0.001425522,0.0001275066,0.00005054925,0.0005137861,0.00021481,0.8655108,0.01834035,0.02968926,0.001919023,0.08180676],"study_design_scores_gemma":[0.000007153069,0.00004945135,0.0003083614,0.000003866587,0.000007578854,0.00006516184,0.00007221844,0.9888787,0.001211542,0.008573209,0.0008160949,0.00000667324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.12331,0.0004876253,0.8696921,0.0002559819,0.0001016884,0.00008896134,0.00003474615,0.000193968,0.005834842],"genre_scores_gemma":[0.9621887,0.0001632957,0.03606198,0.00006390898,0.00002412425,0.00004483267,0.00002377544,0.00001736313,0.001412098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002652378,"threshold_uncertainty_score":0.005273938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0184225674786105,"score_gpt":0.2595259688669305,"score_spread":0.24110340138832,"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."}}