{"id":"W4285049113","doi":"10.1109/tgcn.2022.3190085","title":"Energy-Efficient Resource Allocation for D2D-Assisted Fog Computing","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Energy consumption; Hessian matrix; Benchmark (surveying); Resource allocation; Heuristic; Computational complexity theory; Task (project management); Range (aeronautics); Energy (signal processing); Upper and lower bounds; Computation; Resource (disambiguation); Convex optimization; Distributed computing; Regular polygon; Algorithm; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006866941,0.0001707142,0.0001785445,0.000217399,0.003884237,0.0001505274,0.0012364,0.00004839478,0.000001627327],"category_scores_gemma":[4.281367e-7,0.0001976113,0.000113975,0.0007484224,0.00006924938,0.00009139168,0.0000964649,0.0003517905,0.000001170091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001267711,"about_ca_system_score_gemma":0.00005165553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00015972,"about_ca_topic_score_gemma":0.00002290678,"domain_scores_codex":[0.9984007,0.000277176,0.0003641161,0.000388759,0.0002253873,0.0003437894],"domain_scores_gemma":[0.9977808,0.0006950287,0.0001728602,0.001193785,0.00007410107,0.00008345209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001373655,0.0001875326,0.000009441106,0.000008744448,0.00003436223,5.960489e-7,0.0008737337,0.009381683,0.000109091,0.0006482514,0.0006245367,0.9881083],"study_design_scores_gemma":[0.0003157083,0.0001062493,0.00002019208,0.00003093131,0.00001979047,0.00002457163,0.00006446951,0.8510001,0.00008176891,0.0002429372,0.1479038,0.0001895087],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001314272,0.0007448418,0.9927095,0.002213564,0.001788102,0.0002409651,0.000002213987,0.0002375939,0.0007489285],"genre_scores_gemma":[0.975035,0.00006392035,0.02352809,0.0006968571,0.0003263532,0.0001212735,0.00001549316,0.00002349466,0.0001895059],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9879188,"threshold_uncertainty_score":0.9974126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04226620717303176,"score_gpt":0.2601909038320263,"score_spread":0.2179246966589945,"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."}}