{"id":"W2990735895","doi":"10.1002/ett.3798","title":"Efficient scheduling of video camera sensor networks for IoT systems in smart cities","year":2019,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Thompson Rivers University","funders":"Zayed University; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Probabilistic logic; Computational complexity theory; Scheduling (production processes); Kullback–Leibler divergence; Mathematical optimization; Optimization problem; Real-time computing; Algorithm; Artificial intelligence; Mathematics","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":[0.0006383236,0.0003804091,0.0004149885,0.0002627652,0.0004016835,0.0004878989,0.0005699473,0.0003899784,0.0009117186],"category_scores_gemma":[0.001389771,0.0002238663,0.0002547491,0.0003427519,0.0002981599,0.0006096506,0.0004228208,0.0003871938,0.00008001457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009011385,"about_ca_system_score_gemma":0.001355681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005999898,"about_ca_topic_score_gemma":0.004898133,"domain_scores_codex":[0.9996431,0.0001249607,0.00001109063,0.00006932054,0.00008647518,0.00006497843],"domain_scores_gemma":[0.9995803,0.0001852245,0.00007297444,0.00002235887,0.00009716836,0.00004210081],"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":[0.00003936696,0.00003615852,0.0007954745,0.000019355,0.00001392773,0.00003937271,0.0000218192,0.971864,0.002975409,0.003898573,0.0004180961,0.01987854],"study_design_scores_gemma":[0.000003074344,0.0000130171,0.0001314751,9.482987e-7,0.000001830392,0.000004733294,0.000006845407,0.9985019,0.0004843233,0.0007259109,0.0001244476,0.000001449921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1695309,0.000264323,0.8249587,0.0002317467,0.0000457835,0.00007101035,0.00002847312,0.0002269174,0.004642131],"genre_scores_gemma":[0.9508669,0.00008609713,0.04803248,0.00005454555,0.0000106623,0.00004656784,0.00003842772,0.00001954008,0.0008447318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005999898,"threshold_uncertainty_score":0.01192993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01214241345798896,"score_gpt":0.2403478996836027,"score_spread":0.2282054862256137,"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."}}