{"id":"W4244243901","doi":"10.46300/9107.2020.14.1","title":"Mathematical Modeling of Optimal Node Deployment for Indoor Wireless Sensor Networks","year":2020,"lang":"en","type":"article","venue":"International Journal of Communications","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Universiti Sains Malaysia","keywords":"Wireless sensor network; Software deployment; Computer science; Node (physics); Key distribution in wireless sensor networks; Computer network; Sensor node; Grid; Real-time computing; Event (particle physics); Mobile wireless sensor network; Wireless network; Wireless; Distributed computing; Engineering; 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":[0.000969818,0.001005941,0.0007556378,0.0007300162,0.0004674439,0.001240355,0.001667637,0.001398222,0.002735918],"category_scores_gemma":[0.002930016,0.0005685223,0.0007570144,0.0009826354,0.00102778,0.00171368,0.0008953532,0.001238535,0.0008686731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001449837,"about_ca_system_score_gemma":0.0008665167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004312159,"about_ca_topic_score_gemma":0.002656496,"domain_scores_codex":[0.9995508,0.0001500702,0.00001778051,0.00008442979,0.0001349862,0.00006190945],"domain_scores_gemma":[0.9991899,0.0004520006,0.0001350535,0.00003213776,0.0001549702,0.00003590482],"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":[0.00001199599,0.00001684038,0.000285062,0.00009547945,0.00001268338,0.0000938324,0.000069439,0.8916324,0.001459586,0.09988361,0.001381003,0.005058038],"study_design_scores_gemma":[0.000002916714,0.0000140949,0.00009097312,0.000009975547,0.000004591415,0.00002890531,0.0000158198,0.9838071,0.0001414263,0.01460338,0.001275141,0.000005730322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007303566,0.001072254,0.9771846,0.0007100891,0.00009298925,0.00004177092,0.0001533865,0.00009302243,0.01334826],"genre_scores_gemma":[0.7747067,0.00795459,0.1788485,0.0006022214,0.0003439573,0.0008704912,0.0005048634,0.0002819588,0.03588668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004312159,"threshold_uncertainty_score":0.01051939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05086067950170815,"score_gpt":0.3027575675587318,"score_spread":0.2518968880570236,"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."}}