{"id":"W3000443447","doi":"10.1109/jiot.2020.2964657","title":"An Online Incentive Mechanism for Crowdsensing With Random Task Arrivals","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Computer science; Crowdsensing; Task (project management); Incentive; Online algorithm; Mechanism design; Mechanism (biology); Scheme (mathematics); Focus (optics); Order (exchange); Competitive analysis; Reverse auction; Task analysis; Common value auction; Computer security; Upper and lower bounds; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005743059,0.0002334599,0.0004426212,0.0001170461,0.0001495632,0.0004763718,0.0009267183,0.00008177751,0.000005543829],"category_scores_gemma":[0.0002562527,0.0001854502,0.0001836245,0.0001820038,0.00007713873,0.001032509,0.00007714018,0.0004520471,0.000002778383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004849113,"about_ca_system_score_gemma":0.0001050902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003829616,"about_ca_topic_score_gemma":0.000004269578,"domain_scores_codex":[0.9981336,0.0001240231,0.0005494118,0.0003984098,0.0004278304,0.0003667224],"domain_scores_gemma":[0.9978018,0.0001536815,0.0005974897,0.0002913316,0.0008432226,0.0003125145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002711295,0.0005969491,0.0003102717,0.0002524457,0.0008101924,0.0004934205,0.06923322,0.00582033,0.839664,0.01292312,0.006618926,0.06056588],"study_design_scores_gemma":[0.003983829,0.001797228,0.00003228084,0.0006247676,0.00007603402,0.0005412051,0.0006467035,0.5912036,0.3948558,0.005185749,0.000618526,0.0004342726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3407221,0.00002614673,0.6576018,0.0008479374,0.0005494031,0.0001313744,0.00000286414,0.00006971111,0.00004860299],"genre_scores_gemma":[0.8595684,0.000005873314,0.1379795,0.001911997,0.0004707064,0.000001429648,0.000001621593,0.0000260641,0.00003438441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5853833,"threshold_uncertainty_score":0.756244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266335226112011,"score_gpt":0.2532920130159977,"score_spread":0.2266584904047966,"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."}}