{"id":"W2980350661","doi":"10.1109/lcomm.2019.2948179","title":"Collision-Free Sequential Task Offloading for Mobile Edge Computing","year":2019,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mobile edge computing; Server; Benchmark (surveying); Edge computing; Enhanced Data Rates for GSM Evolution; Latency (audio); Heuristic; Task (project management); Optimization problem; Scheme (mathematics); Mathematical optimization; Distributed computing; Algorithm; Computer network; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003031439,0.0006607075,0.0007061643,0.0002813111,0.0008275778,0.0005323084,0.0008845498,0.0003949898,0.001043115],"category_scores_gemma":[0.0007621105,0.0002291601,0.0003255528,0.00063324,0.0004551412,0.0008392028,0.0008338486,0.0004777309,0.0001718612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004992085,"about_ca_system_score_gemma":0.001074871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00265127,"about_ca_topic_score_gemma":0.004522838,"domain_scores_codex":[0.9995809,0.00006383372,0.00001958061,0.00007360879,0.0001310855,0.0001310551],"domain_scores_gemma":[0.9996589,0.0001223026,0.00004618504,0.0000670275,0.00006487699,0.00004070742],"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":[0.000560731,0.0001612467,0.001068608,0.0001958165,0.00006495527,0.0004086873,0.000174029,0.7931105,0.03731874,0.01954507,0.003095908,0.1442956],"study_design_scores_gemma":[0.0000120312,0.00007382744,0.0001628839,0.000003836546,0.00000894918,0.00007540103,0.00002792756,0.9917767,0.00258907,0.004359701,0.0009019388,0.000007725771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08319852,0.0006460065,0.9125279,0.0001484549,0.0001310683,0.00008316983,0.00003645507,0.0002704563,0.002957987],"genre_scores_gemma":[0.9396886,0.0002025065,0.05855498,0.00005883117,0.00004367066,0.00004910885,0.00003227075,0.00002546728,0.001344546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00265127,"threshold_uncertainty_score":0.005271673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02899697527961946,"score_gpt":0.285949286398784,"score_spread":0.2569523111191645,"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."}}