{"id":"W2741935620","doi":"10.1109/icc.2017.7996593","title":"A scalable overload control algorithm for massive access in machine-to-machine networks","year":2017,"lang":"en","type":"article","venue":"","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Bottleneck; Scalability; Overhead (engineering); Distributed computing; Computer network; Random access; Access control; Blocking (statistics); Algorithm; Embedded system","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":[],"consensus_categories":[],"category_scores_codex":[0.0002571128,0.0002206897,0.0003340473,0.00007165217,0.0001871867,0.0004003163,0.000597452,0.0001394013,0.0001620665],"category_scores_gemma":[0.00002823788,0.0001930245,0.00008233648,0.00007986762,0.00001966797,0.0003407175,0.00009853685,0.000221242,0.00002094279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000564075,"about_ca_system_score_gemma":0.0000121318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003463767,"about_ca_topic_score_gemma":0.0005475349,"domain_scores_codex":[0.9988755,0.00001607824,0.000255317,0.0002322608,0.0001007017,0.0005201578],"domain_scores_gemma":[0.9991909,0.0001044704,0.00005268877,0.0004744108,0.00003916031,0.0001384239],"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":[0.0000932562,0.00003784912,0.004709349,0.00006096088,0.00007231808,0.00001809402,0.00002830915,0.7052099,0.00003689874,0.0004037302,0.01652778,0.2728016],"study_design_scores_gemma":[0.002333321,0.00005613867,0.003433669,0.00006997204,0.000009370001,0.000001301033,0.000001656879,0.9756445,0.0001169447,0.0003626358,0.01770732,0.0002631165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002954701,0.0002454457,0.9765668,0.0002980125,0.0006132846,0.009423673,0.00006435469,0.0001811755,0.01231181],"genre_scores_gemma":[0.9684489,0.00004972227,0.01384133,0.0009018352,0.001053994,0.0137336,0.00003102357,0.0001215685,0.001817978],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9681535,"threshold_uncertainty_score":0.7871312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331545441366458,"score_gpt":0.2859710157209651,"score_spread":0.2726555613073005,"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."}}