{"id":"W4404102940","doi":"10.1109/isse63315.2024.10741146","title":"Dynamic Control for Coverage Maximization in Mobile Sensor Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Maximization; Control (management); Wireless sensor network; Mobile telephony; Mobile computing; Mobile radio; Computer network; Mathematical optimization; Artificial intelligence; Mathematics","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":[0.0002555365,0.0001473516,0.0001709664,0.0001588027,0.00005146605,0.0002758838,0.0003520118,0.0001102751,0.00001674619],"category_scores_gemma":[0.00001415379,0.0001335651,0.00008012092,0.0005793179,0.00001998666,0.0002745614,0.00005689471,0.0001368641,0.00001733883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001008594,"about_ca_system_score_gemma":0.000032671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009328907,"about_ca_topic_score_gemma":0.0000515873,"domain_scores_codex":[0.9986996,0.00005330751,0.0002581953,0.0004727415,0.0001448235,0.0003713191],"domain_scores_gemma":[0.9991131,0.0004163417,0.0000313902,0.0003387693,0.00004499701,0.00005542427],"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":[0.000007144212,0.00002711473,0.0000485062,0.0000132757,0.000009012742,0.00001928099,0.00004684341,0.9494709,0.00007199901,0.02500673,0.0003359783,0.02494318],"study_design_scores_gemma":[0.0004689294,0.00005779709,0.00009877898,0.00003999526,0.000004322471,0.000008038054,0.000005882298,0.9946585,0.00003784506,0.0003380593,0.004116545,0.0001653286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005192394,0.0009603067,0.9906359,0.0002977776,0.001035995,0.0005079374,0.000003387314,0.0004830012,0.0008833179],"genre_scores_gemma":[0.9691232,0.0001094616,0.0287786,0.0003673575,0.00006522872,0.0001384949,0.00001556771,0.00002361071,0.001378465],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9639308,"threshold_uncertainty_score":0.5446627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004651319970061463,"score_gpt":0.2240784909377936,"score_spread":0.2194271709677321,"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."}}