{"id":"W2792703057","doi":"10.1109/tsusc.2018.2816465","title":"A Novel Hierarchical Two-Tier Node Deployment Strategy for Sustainable Wireless Sensor Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Computing","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Wireless sensor network; Computer science; Energy harvesting; Energy consumption; Software deployment; Computer network; Node (physics); Sensor node; Key distribution in wireless sensor networks; Wireless; Energy (signal processing); Distributed computing; Wireless network; Telecommunications; Engineering; Electrical engineering","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":[0.0004444579,0.000709964,0.0005757276,0.0004721912,0.0006256754,0.000469215,0.001386828,0.0005094857,0.001280396],"category_scores_gemma":[0.0008871986,0.0002844244,0.0004956628,0.0006045376,0.0003539889,0.001171117,0.001122073,0.0003330294,0.000448269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005268835,"about_ca_system_score_gemma":0.0006980305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001904381,"about_ca_topic_score_gemma":0.004481961,"domain_scores_codex":[0.9996322,0.00008733293,0.00002107825,0.00009970443,0.00009864061,0.00006091288],"domain_scores_gemma":[0.9996185,0.00009047333,0.00005193866,0.00007771623,0.0001074354,0.00005379469],"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":[0.0002388571,0.0001840115,0.002832096,0.000411653,0.0001012231,0.0007355501,0.0004550283,0.5633867,0.1181805,0.04142579,0.00783017,0.2642184],"study_design_scores_gemma":[0.00001270194,0.0001887069,0.0006676891,0.00001142498,0.0000308518,0.0002996582,0.00008197036,0.9849727,0.00521041,0.005260195,0.00323616,0.00002752823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02616023,0.0005296178,0.9691178,0.0002033944,0.00007935154,0.00009740637,0.00006188254,0.0004938805,0.003256405],"genre_scores_gemma":[0.8164465,0.0005666061,0.1790684,0.0001676639,0.00004996474,0.0001363498,0.0001341054,0.00005253337,0.003377959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001904381,"threshold_uncertainty_score":0.004283369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01577926929922393,"score_gpt":0.2507152665344926,"score_spread":0.2349359972352687,"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."}}