{"id":"W4313053000","doi":"10.1109/tvt.2022.3224443","title":"Learning Aided Joint Sensor Activation and Mobile Charging Vehicle Scheduling for Energy-Efficient WRSN-Based Industrial IoT","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless sensor network; Scheduling (production processes); Energy consumption; Wireless; Real-time computing; Efficient energy use; Distributed computing; Computer network; Mathematical optimization; Engineering; Electrical engineering; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002585883,0.000243793,0.0002733119,0.0006146402,0.0007233588,0.00003084462,0.0001439779,0.0003145137,0.00002600369],"category_scores_gemma":[0.00002442984,0.0002991049,0.00009839434,0.0006743308,0.00007928701,0.00003664052,0.000005634795,0.001121858,0.000001552327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858132,"about_ca_system_score_gemma":0.00003909869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001677281,"about_ca_topic_score_gemma":0.000003483925,"domain_scores_codex":[0.9985824,0.00007748707,0.0003195624,0.0003921783,0.0002088706,0.0004194833],"domain_scores_gemma":[0.9993577,0.0001867379,0.00008393991,0.000259406,0.00005086367,0.00006138174],"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":[0.00003771211,0.00006454133,0.00001084813,0.00002010503,0.00004853939,0.00000466506,0.00005188247,0.8721073,0.1002079,0.000109677,0.000006567845,0.02733034],"study_design_scores_gemma":[0.0008787959,0.0002282097,0.00000331664,0.00004663094,0.00002649547,0.00001056475,0.0002282001,0.7142456,0.2824295,0.00002218653,0.001665052,0.0002154365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5316594,0.00005388049,0.4667638,0.0001267358,0.0003075282,0.0002096667,0.000007383357,0.0008638725,0.000007778155],"genre_scores_gemma":[0.9949552,0.00001049583,0.003565868,0.0000503919,0.00007723386,0.001192738,0.0000130603,0.00009373207,0.00004122944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4632959,"threshold_uncertainty_score":0.9999461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453820111665381,"score_gpt":0.20339237953044,"score_spread":0.1888541784137862,"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."}}