{"id":"W3040935040","doi":"10.1109/access.2020.3008289","title":"Learning-Based IoT Data Aggregation for Disaster Scenarios","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Deanship of Scientific Research, King Saud University","keywords":"Computer science; Data aggregator; Quality of service; Automation; Energy consumption; Reinforcement learning; Software deployment; Distributed computing; Wireless sensor network; Computer security; Artificial intelligence; Computer network; Software engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001727893,0.0001471646,0.0001565913,0.00004812117,0.0001571295,0.0004746568,0.003306758,0.00007193988,0.000009623905],"category_scores_gemma":[0.0001395927,0.0001400457,0.0000464417,0.0004397971,0.00003580595,0.0004790293,0.0004448867,0.0001741641,0.00003043808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000207722,"about_ca_system_score_gemma":0.00006107454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000151786,"about_ca_topic_score_gemma":0.00001976988,"domain_scores_codex":[0.9984764,0.00007082173,0.0002137111,0.0006619776,0.0002756974,0.0003013215],"domain_scores_gemma":[0.9985443,0.000232603,0.0001427117,0.0008597934,0.00009000413,0.0001306171],"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":[0.00001843802,0.00003161839,0.0005679336,0.00002638212,0.00000876216,0.000005051629,0.0001979108,0.9739728,0.0002041234,0.0002235324,0.004407527,0.02033593],"study_design_scores_gemma":[0.0004433278,0.00007827519,0.00008672274,0.00002804199,0.000007627332,7.38835e-7,0.000006081228,0.9840786,0.003357599,0.00002882967,0.01170221,0.0001819259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05468555,0.00003659364,0.9402156,0.003786266,0.0006191489,0.0002329132,0.00000770008,0.000296788,0.0001194135],"genre_scores_gemma":[0.9823788,0.000001949169,0.01436192,0.002643922,0.0004413323,0.00002024008,0.00005553308,0.00002266989,0.00007363361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9276932,"threshold_uncertainty_score":0.6144834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09075499164893248,"score_gpt":0.3150970170699442,"score_spread":0.2243420254210117,"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."}}