{"id":"W3025641346","doi":"10.1016/j.comcom.2020.05.020","title":"Green communication in IoT networks using a hybrid optimization algorithm","year":2020,"lang":"en","type":"article","venue":"Computer Communications","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Energy consumption; Internet of Things; Optimization problem; Wireless sensor network; Residual; Energy (signal processing); Distributed computing; Mathematical optimization; Computer network; Algorithm; Embedded system","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":[0.0006807885,0.000589315,0.0008540412,0.0005192373,0.000482966,0.0009253487,0.0008219406,0.0009653686,0.002081558],"category_scores_gemma":[0.000839394,0.0002929399,0.0006254432,0.0008095102,0.000576783,0.0008163848,0.0008819381,0.0006365563,0.0001871657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00066911,"about_ca_system_score_gemma":0.0006953663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002594759,"about_ca_topic_score_gemma":0.002605131,"domain_scores_codex":[0.9997697,0.0000900509,0.00000679488,0.00003253102,0.00006468758,0.00003621053],"domain_scores_gemma":[0.9996245,0.000247273,0.00002908179,0.0000187902,0.00005718858,0.00002309777],"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":[0.0000277438,0.00002124042,0.0001142597,0.00001276434,0.00001846046,0.00001407899,0.000008529939,0.9844825,0.0003549031,0.005660525,0.0004260114,0.008858977],"study_design_scores_gemma":[0.000002358524,0.000006228727,0.00001663376,0.000001075611,0.000001870421,0.000002353659,0.000001997654,0.9987183,0.00004003852,0.001132605,0.00007565736,9.746053e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02427173,0.0003427339,0.9675243,0.0002599117,0.00008960671,0.00004137175,0.00003000487,0.0001350877,0.007305307],"genre_scores_gemma":[0.7667066,0.0003890969,0.2243336,0.0002173256,0.0000962393,0.0002078158,0.00009564182,0.0001028121,0.007850804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002594759,"threshold_uncertainty_score":0.006963432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03996381031355054,"score_gpt":0.253364150651817,"score_spread":0.2134003403382665,"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."}}