{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003401557,0.00138148,0.00190448,0.001052927,0.000949726,0.001928347,0.003229528,0.002452123,0.004228442],"category_scores_gemma":[0.009138915,0.0009886718,0.001185126,0.001483338,0.001383375,0.00235239,0.00197792,0.002075596,0.0004624272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002065994,"about_ca_system_score_gemma":0.004006621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003519063,"about_ca_topic_score_gemma":0.002478477,"domain_scores_codex":[0.9977838,0.0007702076,0.0001278643,0.0005147637,0.0004594513,0.0003438267],"domain_scores_gemma":[0.9952749,0.003018834,0.0005280481,0.0003667196,0.000510508,0.0003008486],"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":[0.0002224404,0.000110795,0.00063614,0.0003436948,0.00006745671,0.0002454087,0.0002114771,0.8495428,0.002830491,0.08012163,0.002493921,0.06317376],"study_design_scores_gemma":[0.00004602607,0.0000768021,0.00008951928,0.00002152638,0.00001904424,0.00007910656,0.00004100176,0.9659223,0.0006360324,0.03121148,0.001839884,0.0000172239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008975886,0.0004311476,0.9872482,0.000388734,0.00007971073,0.0001852842,0.00009803166,0.0002864417,0.002306457],"genre_scores_gemma":[0.6858787,0.0007859719,0.3076873,0.0002832371,0.0001060277,0.0005826641,0.0002334233,0.00008985917,0.004352765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004228442,"threshold_uncertainty_score":0.0179894,"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."}}