{"id":"W3146005551","doi":"10.1109/wsc.2009.5429272","title":"Simulation of Large Wireless Sensor Networks using Cell-DEVS","year":2009,"lang":"en","type":"article","venue":"Proceedings of the 2009 Winter Simulation Conference (WSC)","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Ottawa","funders":"","keywords":"Wireless sensor network; DEVS; Computer science; Key distribution in wireless sensor networks; Wireless; Formalism (music); Wireless ad hoc network; Computer network; Mobile wireless sensor network; Topology control; Distributed computing; Wireless network; Modeling and simulation; Simulation; Telecommunications","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":[0.0007881731,0.0005024771,0.0008885073,0.0005804451,0.00069185,0.0008943574,0.001476325,0.001118095,0.002659869],"category_scores_gemma":[0.003059972,0.000347529,0.0007886675,0.0009678326,0.000735861,0.0006589078,0.0008073312,0.0008973689,0.000215625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009991556,"about_ca_system_score_gemma":0.0008221082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01612236,"about_ca_topic_score_gemma":0.008086705,"domain_scores_codex":[0.9996789,0.0001128585,0.00001995616,0.00003838837,0.00008223257,0.00006775165],"domain_scores_gemma":[0.9974098,0.00185817,0.0001542455,0.0001415685,0.0002914523,0.000144841],"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":[0.00001718167,0.00001757914,0.0005891051,0.00001472759,0.000009815099,0.00002773789,0.00002464014,0.9945171,0.0002608398,0.003655746,0.0001322062,0.0007332108],"study_design_scores_gemma":[0.000005046978,0.000005269396,0.00005981931,0.000001564422,0.000001788744,0.000003900378,0.000007384864,0.9990871,0.0001515204,0.0005152164,0.0001592265,0.000002134978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6902334,0.0007188652,0.280528,0.001003884,0.0002417598,0.0002043914,0.002532586,0.001200874,0.02333625],"genre_scores_gemma":[0.9444465,0.0004184491,0.05040386,0.0001398137,0.0000237385,0.000390849,0.001166738,0.00009675959,0.002913292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01612236,"threshold_uncertainty_score":0.03205705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276416926096782,"score_gpt":0.2664443784859443,"score_spread":0.2436802092249765,"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."}}