{"id":"W3000582759","doi":"10.1002/ett.3860","title":"TD‐PSO: Task distribution approach based on particle swarm optimization for vehicular ad hoc network","year":2020,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Particle swarm optimization; Overhead (engineering); Vehicular ad hoc network; Task (project management); Broadcasting (networking); Resource allocation; Distributed computing; Genetic algorithm; Mathematical optimization; Wireless ad hoc network; Computer network; Machine learning; Wireless; Engineering","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":[0.0005922599,0.0007309699,0.0007632138,0.0005196277,0.0003798509,0.0005920889,0.0007891554,0.0006931931,0.001342013],"category_scores_gemma":[0.001255976,0.000300764,0.0005265295,0.0004855354,0.0003755501,0.0005036579,0.0005218069,0.0006985974,0.0001810127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005415848,"about_ca_system_score_gemma":0.001060026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008370061,"about_ca_topic_score_gemma":0.004367743,"domain_scores_codex":[0.999727,0.00008655388,0.00001664609,0.00004519972,0.00008197485,0.00004262992],"domain_scores_gemma":[0.9995958,0.0001930022,0.00004941378,0.00002271884,0.0001073847,0.0000317153],"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":[0.00003178818,0.00003445494,0.0003718711,0.0000310334,0.00002229401,0.00002398121,0.00001953878,0.9716431,0.0008441401,0.002302013,0.0007126866,0.02396305],"study_design_scores_gemma":[0.000004343683,0.00001494285,0.0000399269,0.000001613699,0.000002390187,0.000003483048,0.000003742226,0.9993132,0.00009439477,0.00035074,0.0001698302,0.000001388591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01671382,0.0003377629,0.9792833,0.0001911614,0.0001012833,0.00006464825,0.00002427018,0.0002262451,0.003057526],"genre_scores_gemma":[0.7807935,0.0003685758,0.2146031,0.0001545321,0.00007556754,0.0002567286,0.0001110038,0.00006063406,0.003576281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008370061,"threshold_uncertainty_score":0.01664269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01638004648638708,"score_gpt":0.2231951249098054,"score_spread":0.2068150784234184,"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."}}