{"id":"W2783035441","doi":"10.1109/glocom.2017.8255038","title":"Latency Minimization in Wireless IoT Using Prioritized Channel Access and Data Aggregation","year":2017,"lang":"en","type":"article","venue":"","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Computer network; Latency (audio); Cloud computing; Network packet; Queueing theory; Access control; Channel (broadcasting); Data aggregator; Wireless; Wireless sensor network; Low latency (capital markets); Distributed computing; 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":[0.0006641165,0.0005407654,0.0003826589,0.0005708957,0.0007439219,0.0007725253,0.0007180126,0.0002605598,0.0004396742],"category_scores_gemma":[0.001211328,0.0001853215,0.0002280683,0.0006709043,0.0003428567,0.0009962546,0.0005248624,0.0003543311,0.00007561218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000909221,"about_ca_system_score_gemma":0.001306015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004647194,"about_ca_topic_score_gemma":0.005730146,"domain_scores_codex":[0.9994847,0.00009680483,0.00002753549,0.00008382699,0.0001629478,0.0001441477],"domain_scores_gemma":[0.9992211,0.0003165766,0.000115797,0.00006909015,0.0002172952,0.00006016744],"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":[0.001161713,0.0006491282,0.0059439,0.0003579342,0.0001415023,0.0004704095,0.0004402414,0.5454869,0.1372813,0.02020033,0.002792543,0.2850741],"study_design_scores_gemma":[0.00001968451,0.0002268541,0.0007325089,0.000005868865,0.0000355595,0.00008146329,0.00006718687,0.9855952,0.009669957,0.002810552,0.0007376273,0.00001748369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2557604,0.002245458,0.7376432,0.0002352209,0.0001697057,0.0001093219,0.00004641484,0.0006286117,0.00316161],"genre_scores_gemma":[0.9714313,0.0003400556,0.02762802,0.00004727009,0.00004476907,0.00002117084,0.00002109786,0.00001577133,0.000450479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004647194,"threshold_uncertainty_score":0.00924027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1325132510934263,"score_gpt":0.3654721891284062,"score_spread":0.2329589380349799,"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."}}