{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002150708,0.0001972162,0.0003328546,0.0006726601,0.0001356824,0.00001887103,0.0004491404,0.0001983568,0.000007229239],"category_scores_gemma":[0.00004231518,0.0002207876,0.0000839669,0.0007977846,0.00008484662,0.00004533526,0.0000118767,0.0003889718,0.000007643849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001848241,"about_ca_system_score_gemma":0.00002045151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005386306,"about_ca_topic_score_gemma":0.00004850505,"domain_scores_codex":[0.9988246,0.00003622263,0.0005594623,0.000199515,0.00009014846,0.0002900324],"domain_scores_gemma":[0.998347,0.0003563934,0.000111053,0.001063499,0.0001060686,0.00001597844],"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":[0.000008407947,0.00004655768,0.0001147452,0.0001252946,0.00003855383,7.641492e-8,0.0001665516,0.9930474,0.001227323,0.0008746192,0.000005670568,0.004344873],"study_design_scores_gemma":[0.0003586358,0.000041824,0.00003573817,0.0002796414,0.000016669,0.000002492579,0.004889089,0.9910216,0.00283433,0.00005423138,0.0002567443,0.0002089702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1011357,0.002001915,0.893436,0.0002194485,0.0003013898,0.001011314,0.00002151131,0.001613421,0.0002592444],"genre_scores_gemma":[0.906019,0.0004273255,0.09294185,0.000003034874,0.000004557697,0.0004869498,0.000009651767,0.00004923894,0.0000583671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8048833,"threshold_uncertainty_score":0.9003459,"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."}}