{"id":"W2904570205","doi":"10.3390/s18124408","title":"Efficient Path Planning and Truthful Incentive Mechanism Design for Mobile Crowdsensing","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crowdsensing; Incentive; Mechanism (biology); Path (computing); Mechanism design; Computer science; Motion planning; Computer security; Risk analysis (engineering); Business; Computer network; Artificial intelligence; Economics; Microeconomics; Robot","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":[0.000615142,0.0002199729,0.0002342456,0.0001314088,0.0005641499,0.0002580755,0.0002228856,0.00009667136,0.000002629467],"category_scores_gemma":[0.00007059597,0.0002083367,0.00006627416,0.0002422673,0.0001455424,0.00006982918,0.0001451681,0.0001134026,0.00001595151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004833156,"about_ca_system_score_gemma":0.00004514704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001965322,"about_ca_topic_score_gemma":0.000001182539,"domain_scores_codex":[0.9983789,0.0001170302,0.000241718,0.0005349125,0.0002147096,0.0005127321],"domain_scores_gemma":[0.9987884,0.0003470963,0.000121084,0.0004199065,0.0001824158,0.0001410793],"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":[0.0005668298,0.0005276583,0.0007228441,0.0003185647,0.0003713189,0.0003888102,0.1692734,0.3704636,0.1797523,0.08311301,0.003236662,0.1912649],"study_design_scores_gemma":[0.0004510921,0.0003540214,0.0001111816,0.0001142695,0.00001501329,0.00005993884,0.001140782,0.9440946,0.05107658,0.001735904,0.0005485043,0.0002981256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5050229,0.00005237637,0.4939624,0.00003073471,0.0003500495,0.0003031852,0.000001682373,0.0001452141,0.0001314994],"genre_scores_gemma":[0.8938502,0.00000136567,0.1057098,0.0001254844,0.0001896712,0.00001494714,9.691724e-7,0.00002324973,0.00008428499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5736309,"threshold_uncertainty_score":0.8495724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087792937710901,"score_gpt":0.2556616973828725,"score_spread":0.2347837680057635,"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."}}