{"id":"W4408100372","doi":"10.1109/tsc.2025.3547221","title":"Service Migration for Delay-Sensitive IoT Applications in Edge Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Fundamental Research Funds for the Central Universities; China Geological Survey; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Computer network; Enhanced Data Rates for GSM Evolution; Internet of Things; Service (business); Edge computing; Distributed computing; Telecommunications; Computer security","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":[0.0002792632,0.0002676515,0.0002731185,0.0004071911,0.0005125428,0.0002191123,0.0007409627,0.0001823998,0.000001010354],"category_scores_gemma":[0.000001091799,0.0002948949,0.0001162305,0.002272305,0.00002256052,0.0002375988,0.00001292338,0.0003390099,0.000007754781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000136739,"about_ca_system_score_gemma":0.00005540912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003523529,"about_ca_topic_score_gemma":0.005377234,"domain_scores_codex":[0.9980817,0.0001061576,0.0004763945,0.0006898699,0.0001786459,0.0004671846],"domain_scores_gemma":[0.9981291,0.0007823975,0.0001470202,0.0005920607,0.0002766149,0.00007281323],"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":[0.00001626694,0.0001520638,0.00003773425,0.00006457489,0.00002719263,0.000001256088,0.0006108058,0.9549036,0.0001496989,0.002292989,0.00001069813,0.0417331],"study_design_scores_gemma":[0.0004860525,0.00003142611,0.0002977911,0.0002217517,0.00002108814,0.000002973665,0.0003123701,0.9951102,0.002507227,0.0001137779,0.0006555381,0.0002398211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06525272,0.00007195548,0.9319233,0.0007357608,0.0007796421,0.0006817313,0.000004459445,0.0003453238,0.0002051221],"genre_scores_gemma":[0.9610677,0.0000161058,0.03679067,0.001853531,0.00008546763,0.0001056701,0.00001399328,0.00002026846,0.0000466179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.895815,"threshold_uncertainty_score":0.9999503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00916410333811889,"score_gpt":0.2457617489678958,"score_spread":0.2365976456297769,"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."}}