{"id":"W2898477566","doi":"10.1109/jiot.2018.2877762","title":"Energy-Efficient Sleep Scheduling in WBANs: From the Perspective of Minimum Dominating Set","year":2018,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Beijing Institute of Technology; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Wireless sensor network; Approximation algorithm; Scheduling (production processes); Energy consumption; Efficient energy use; Sleep mode; Mathematical optimization; Algorithm; Computer network; Mathematics","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":[0.000825348,0.0007600953,0.0008009211,0.0004599901,0.0006135979,0.0006118305,0.00121652,0.0006055965,0.0006478065],"category_scores_gemma":[0.002008921,0.0003086669,0.0006222246,0.0006132804,0.0005222333,0.001191287,0.0009823692,0.0007249218,0.0001266697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007013515,"about_ca_system_score_gemma":0.0009104199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001366424,"about_ca_topic_score_gemma":0.001617525,"domain_scores_codex":[0.9995609,0.0001699462,0.00002235417,0.000102497,0.00009394411,0.00005030662],"domain_scores_gemma":[0.9993601,0.0003412595,0.00007405628,0.000064419,0.00008642506,0.00007377087],"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":[0.0001214468,0.00007226008,0.0008579065,0.0002058993,0.00005264037,0.0001075484,0.0002603079,0.8981807,0.007965783,0.03022923,0.001762474,0.06018378],"study_design_scores_gemma":[0.00001282403,0.00009285139,0.0001637202,0.00001107083,0.00001295371,0.00007264625,0.0000548338,0.9822189,0.001364692,0.01482559,0.001162287,0.000007685558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03087687,0.0004199442,0.9666488,0.0002983095,0.00005370008,0.00008001824,0.0000529562,0.0000821256,0.00148721],"genre_scores_gemma":[0.6569706,0.0008850176,0.3388377,0.000193871,0.00006992721,0.0002411662,0.0002030321,0.00005366549,0.002545013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001366424,"threshold_uncertainty_score":0.005088687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016503207770413,"score_gpt":0.2333610396525039,"score_spread":0.2231960075747998,"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."}}