{"id":"W4408285997","doi":"10.1007/s10207-025-01011-5","title":"Understanding the impact of IoT security patterns on CPU usage and energy consumption: a dynamic approach for selecting patterns with deep reinforcement learning","year":2025,"lang":"en","type":"article","venue":"International Journal of Information Security","topic":"Green IT and Sustainability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Reinforcement learning; Energy consumption; Internet of Things; Consumption (sociology); Computer security; Cryptography; Energy (signal processing); Embedded system; Distributed computing; 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":[0.0005604572,0.0006437859,0.000442263,0.0004658281,0.0001370418,0.0006781769,0.000648588,0.0005003657,0.0007772012],"category_scores_gemma":[0.004217065,0.0003006794,0.0003034426,0.0004265583,0.0002914556,0.001131987,0.0003743987,0.001257831,0.0001553868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005635365,"about_ca_system_score_gemma":0.0005229287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00542944,"about_ca_topic_score_gemma":0.00953851,"domain_scores_codex":[0.9997366,0.00005770452,0.00001487017,0.0001045088,0.00004268659,0.00004362638],"domain_scores_gemma":[0.9985806,0.0009344552,0.0001693505,0.00008895595,0.0001532205,0.00007347693],"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":[0.000471663,0.000693387,0.0779719,0.0001143292,0.000192884,0.0001551649,0.0001210871,0.7950719,0.00849265,0.003083063,0.002029173,0.1116028],"study_design_scores_gemma":[0.000002977726,0.00001671648,0.002954163,0.000003075354,0.00000644198,0.000007660627,0.000009362796,0.9949691,0.0003622634,0.001610868,0.00005454772,0.000002876335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.799015,0.0004717824,0.1965166,0.0009343707,0.00006569095,0.0000482363,0.0005480078,0.0003781193,0.002022133],"genre_scores_gemma":[0.991306,0.00006221183,0.008015064,0.000044954,0.000008934089,0.00001451683,0.000156206,0.00001696448,0.0003751176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00542944,"threshold_uncertainty_score":0.01079571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01342992712764962,"score_gpt":0.2630482604153592,"score_spread":0.2496183332877096,"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."}}