{"id":"W3093886635","doi":"10.1109/jiot.2020.3032537","title":"Analysis of the Interdelivery Time in IoT Energy Harvesting Wireless Sensor Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Capacitor; Energy harvesting; Performance metric; Metric (unit); Wireless; Transmission (telecommunications); Probability distribution; Internet of Things; Real-time computing; Computer network; Energy (signal processing); Electrical engineering; Telecommunications; Mathematics; Engineering; Statistics; Embedded system","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":[],"consensus_categories":[],"category_scores_codex":[0.0004052297,0.000239698,0.0006316002,0.0002804887,0.00003029609,0.00006384096,0.0008178224,0.000162017,0.00006504385],"category_scores_gemma":[0.0001223317,0.0001999588,0.0003781194,0.001054539,0.000105123,0.0001974494,0.0001289868,0.0008692977,0.00000162855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009663483,"about_ca_system_score_gemma":0.00002240882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002320878,"about_ca_topic_score_gemma":0.00007719576,"domain_scores_codex":[0.998013,0.0001509189,0.0009800616,0.0001937319,0.0003308545,0.0003314766],"domain_scores_gemma":[0.9988552,0.0002652838,0.000402292,0.0002221868,0.0001245954,0.0001304809],"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":[0.00002671255,0.00001714896,0.005748508,0.00002988299,0.0007318201,0.00002583731,0.001122186,0.9776219,0.009845079,0.00002439502,0.000612171,0.004194397],"study_design_scores_gemma":[0.0002193818,0.00003900455,0.001426965,0.000574536,0.0002164383,0.00003325639,0.00004575339,0.9861382,0.01101739,0.000009092993,0.0001035949,0.00017642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279545,0.000284705,0.07015751,0.0001050635,0.0006932609,0.00003428896,0.00000241139,0.00007278751,0.0006954594],"genre_scores_gemma":[0.9981959,0.0000575834,0.0009642766,0.0002531448,0.0002671343,0.000001577021,0.000001788527,0.00005000728,0.0002085927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07024138,"threshold_uncertainty_score":0.8154085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008981170369937988,"score_gpt":0.1887491502428907,"score_spread":0.1797679798729527,"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."}}