{"id":"W2900068116","doi":"10.1109/jiot.2018.2880463","title":"Coverage-Guaranteed and Energy-Efficient Participant Selection Strategy in Mobile Crowdsensing","year":2018,"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":"University of British Columbia","funders":"National Research Foundation of Korea; Korea University; National Research Foundation","keywords":"Computer science; Crowdsensing; Markov decision process; Curse of dimensionality; Heuristic; Energy consumption; Selection (genetic algorithm); Greedy algorithm; Process (computing); Artificial intelligence; Markov process; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002529223,0.001103017,0.001481677,0.0006186187,0.001107199,0.0009425647,0.002415304,0.00128139,0.001073276],"category_scores_gemma":[0.006373009,0.0004556046,0.0006691827,0.0006699108,0.001145627,0.001655698,0.002369425,0.0009001666,0.0002742739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006880999,"about_ca_system_score_gemma":0.001629732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002350546,"about_ca_topic_score_gemma":0.001961625,"domain_scores_codex":[0.9971624,0.001146405,0.0001065686,0.0005905115,0.0005822937,0.0004116891],"domain_scores_gemma":[0.9956849,0.002786011,0.0003293199,0.0003655781,0.0004735473,0.0003606023],"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":[0.001250788,0.0003926234,0.005241798,0.0005152345,0.0001828787,0.001382368,0.001170207,0.7324774,0.04536476,0.04738317,0.003872859,0.1607659],"study_design_scores_gemma":[0.00006691638,0.0002504457,0.000453547,0.00001627827,0.00003123746,0.0002709563,0.0002048487,0.9731227,0.004640731,0.01927073,0.001633817,0.00003783701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05320055,0.0003638628,0.9432768,0.0003286016,0.00006143476,0.0002072741,0.00006967634,0.0003989564,0.002092781],"genre_scores_gemma":[0.945977,0.0001456827,0.05253169,0.0001279751,0.00003284515,0.0001914961,0.00004963449,0.00003155195,0.0009121265],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002529223,"threshold_uncertainty_score":0.01337594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036625328084132,"score_gpt":0.258206805280859,"score_spread":0.2378405520000177,"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."}}