{"id":"W2895934437","doi":"10.1109/access.2018.2874134","title":"Resource Management for Cognitive IoT Systems With RF Energy Harvesting in Smart Cities","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"King Abdulaziz University","keywords":"Cognitive radio; Computer science; Quality of service; Efficient energy use; Computer network; Node (physics); Energy harvesting; Throughput; Resource allocation; Resource management (computing); Radio resource management; Distributed computing; Energy (signal processing); Wireless; Wireless network; Telecommunications; Engineering; Electrical 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":[0.0000773746,0.0001225208,0.000143054,0.0001755115,0.00007543264,0.00007871738,0.0004731615,0.00005285823,0.000001758814],"category_scores_gemma":[0.00002167323,0.0001177747,0.00001320535,0.0002701621,0.0001153405,0.000145389,0.00009276051,0.00008162791,0.000002613597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007797661,"about_ca_system_score_gemma":0.000004122748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001110383,"about_ca_topic_score_gemma":0.0003175853,"domain_scores_codex":[0.9993595,0.00001351352,0.0001754599,0.0001490939,0.00008687779,0.0002155374],"domain_scores_gemma":[0.9993979,0.000160332,0.0000498699,0.0003103612,0.00006307261,0.00001851203],"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":[0.0002485495,0.00008690795,0.01092511,0.001571771,0.0004344566,0.0000402782,0.001122929,0.5840922,0.001063694,0.03940206,0.002564091,0.3584479],"study_design_scores_gemma":[0.006514787,0.0006311985,0.01140164,0.006664988,0.0001186294,0.00003767922,0.01901995,0.4488415,0.291214,0.005812347,0.2067847,0.002958505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3566104,0.0006066816,0.6175265,0.00005752538,0.0002243496,0.0005105116,0.00001673538,0.001284857,0.02316245],"genre_scores_gemma":[0.9970048,0.00004491063,0.001914404,0.00003612462,0.00004805048,0.0004841965,0.000005329187,0.00003769578,0.0004245116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6403944,"threshold_uncertainty_score":0.4802715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03022604074495781,"score_gpt":0.2736865744662523,"score_spread":0.2434605337212944,"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."}}