{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006582201,0.001553598,0.002034289,0.001224955,0.00144137,0.002354462,0.004517853,0.00316173,0.003590679],"category_scores_gemma":[0.01798109,0.0008158673,0.001261218,0.001207711,0.001862394,0.00447293,0.003141419,0.002243193,0.0005109025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312428,"about_ca_system_score_gemma":0.002781011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006740842,"about_ca_topic_score_gemma":0.0005272085,"domain_scores_codex":[0.9940363,0.002417322,0.0004027123,0.001290674,0.001220559,0.0006324847],"domain_scores_gemma":[0.9897724,0.005893371,0.001623206,0.001195298,0.0009096014,0.0006061621],"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":[0.001156717,0.001000017,0.001835901,0.001073341,0.0002372055,0.001412958,0.0008662723,0.4265628,0.02541606,0.3828415,0.005984577,0.1516127],"study_design_scores_gemma":[0.0001847307,0.0002944759,0.0001956506,0.00004419766,0.00004713169,0.0003790037,0.00006719615,0.9097123,0.002575406,0.08241112,0.004019224,0.00006952631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009889809,0.0002364121,0.9868396,0.000358193,0.0001520046,0.0002716814,0.00006617344,0.0002614237,0.001924678],"genre_scores_gemma":[0.7787311,0.0003761792,0.2152357,0.0004218444,0.000244765,0.0006720043,0.00009521967,0.00006111082,0.004162054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006582201,"threshold_uncertainty_score":0.03481042,"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."}}