{"id":"W2051751904","doi":"10.1109/iri.2014.7051910","title":"Particle swarm optimization protocol for clustering in wireless sensor networks: A realistic approach","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wireless sensor network; Computer science; Cluster analysis; Particle swarm optimization; Energy consumption; Node (physics); Efficient energy use; Key distribution in wireless sensor networks; Routing protocol; Topology control; Swarm behaviour; Mobile wireless sensor network; Protocol (science); Computer network; Network topology; Distributed computing; Wireless; Wireless network; Algorithm; Routing (electronic design automation); Engineering; Artificial intelligence","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":[0.0009760421,0.0005788289,0.0007264647,0.0003640649,0.0005874293,0.0008505668,0.001219664,0.001263444,0.000904044],"category_scores_gemma":[0.001872539,0.0002650167,0.0005908397,0.0008992908,0.0005962595,0.001045681,0.0009600573,0.001347439,0.0002500551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007367616,"about_ca_system_score_gemma":0.0009665811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001956449,"about_ca_topic_score_gemma":0.00186084,"domain_scores_codex":[0.9993389,0.0002832444,0.00003273321,0.00007807628,0.0002296729,0.00003728897],"domain_scores_gemma":[0.9996703,0.0001460416,0.0000382518,0.00004191854,0.00008542602,0.00001810413],"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":[0.00004007427,0.00005861927,0.0002684207,0.0001294009,0.00003867365,0.0001213857,0.0000883631,0.892192,0.001806594,0.0756008,0.002616782,0.02703879],"study_design_scores_gemma":[0.00001232637,0.00004023913,0.00005139033,0.00000779179,0.000006713939,0.00003137535,0.0000103918,0.9893643,0.0002679452,0.007447586,0.002753927,0.000006014486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004986524,0.000807292,0.988891,0.000632417,0.0001357732,0.0001685611,0.00003526597,0.00011105,0.004232095],"genre_scores_gemma":[0.4621479,0.003228207,0.5240958,0.0004480691,0.0002073558,0.0012601,0.0002658762,0.00007699132,0.008269701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001956449,"threshold_uncertainty_score":0.005345583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02351730726241483,"score_gpt":0.26603408412263,"score_spread":0.2425167768602152,"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."}}