{"id":"W4312354825","doi":"10.1109/tmc.2022.3220720","title":"MOTO: Mobility-Aware Online Task Offloading With Adaptive Load Balancing in Small-Cell MEC","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Calgary","funders":"Key Research and Development Program of Hunan Province of China; Higher Education Discipline Innovation Project; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Computer science; Server; Task (project management); Load balancing (electrical power); Mobile edge computing; Enhanced Data Rates for GSM Evolution; Limiting; Artificial intelligence; Computer network; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009299974,0.0004290008,0.0004771193,0.0004354166,0.001175505,0.0001516731,0.001136149,0.00008402446,0.00001496233],"category_scores_gemma":[0.000005127328,0.0004540636,0.0001747863,0.001706477,0.0000585125,0.0002971098,0.00007441606,0.001368471,0.00001759064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009599427,"about_ca_system_score_gemma":0.0004047168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004330824,"about_ca_topic_score_gemma":0.0001199086,"domain_scores_codex":[0.9964226,0.0003368109,0.0006535223,0.001118586,0.0005968864,0.000871527],"domain_scores_gemma":[0.9981371,0.0005311397,0.0002362847,0.000772109,0.0001554716,0.0001679643],"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":[0.00006404418,0.0009824954,0.0002553344,0.00004849015,0.00003465339,0.0001063887,0.004254371,0.9157004,0.001167539,0.00001826153,0.00006278099,0.07730524],"study_design_scores_gemma":[0.001313059,0.000861434,0.0002388809,0.0001560306,0.00002039172,0.00009106351,0.00113749,0.9915008,0.003411639,0.00006743162,0.000604855,0.0005969236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3775372,0.00006623697,0.6187878,0.00004315443,0.002552334,0.0005096758,0.000004950479,0.0003244728,0.0001741633],"genre_scores_gemma":[0.9773885,0.000003776925,0.02181118,0.0002447568,0.0002821549,0.000100001,0.000004435984,0.00004857631,0.0001166364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5998513,"threshold_uncertainty_score":0.9997911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455674706783932,"score_gpt":0.2249536827157202,"score_spread":0.2103969356478809,"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."}}