{"id":"W2282452766","doi":"10.1016/j.comnet.2016.01.015","title":"Multi-objective optimization in sensor networks: Optimization classification, applications and solution approaches","year":2016,"lang":"en","type":"article","venue":"Computer Networks","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Wireless sensor network; Optimization problem; Mathematical optimization; Software deployment; Resource allocation; Engineering optimization; Multi-objective optimization; Distributed computing; Operations research; Machine learning; Computer network; Algorithm","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":[0.002444527,0.001550552,0.001702114,0.001607051,0.0004910844,0.002099804,0.001522519,0.001728996,0.001885622],"category_scores_gemma":[0.004029013,0.00055457,0.001329349,0.003198536,0.0008499569,0.002049649,0.001159666,0.002042339,0.0004252041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009240189,"about_ca_system_score_gemma":0.0009762825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001926949,"about_ca_topic_score_gemma":0.001593319,"domain_scores_codex":[0.9991357,0.0003529729,0.00006096419,0.0001474694,0.0002399864,0.00006287956],"domain_scores_gemma":[0.9984117,0.0009370531,0.0002517904,0.0001021807,0.0002475987,0.00004966289],"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":[0.00005887766,0.0002126722,0.001434034,0.0008023173,0.0001753265,0.00004413848,0.00008703646,0.6988204,0.0007980295,0.09796623,0.006014018,0.1935869],"study_design_scores_gemma":[0.000006235568,0.00003688993,0.0003870457,0.00005772099,0.00002449486,0.00002777061,0.00002936038,0.9542605,0.000277381,0.04194963,0.002933214,0.00000972207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0071101,0.01366826,0.9728404,0.001197922,0.0001769616,0.00007589156,0.0000988788,0.00008773302,0.004743854],"genre_scores_gemma":[0.355913,0.03679552,0.5912877,0.0006706216,0.001667739,0.0006858431,0.0005616104,0.0002073973,0.01221061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002444527,"threshold_uncertainty_score":0.01292801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02946203928961885,"score_gpt":0.2223362674008998,"score_spread":0.192874228111281,"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."}}