{"id":"W3016835749","doi":"10.1109/tmc.2019.2910074","title":"Energy Efficient Scheduling Algorithms for Sweep Coverage in Mobile Sensor Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Natural Science Foundation of China; Shanghai Science and Technology Development Foundation","keywords":"Computer science; Wireless sensor network; Algorithm; Scheduling (production processes); Scalability; Schedule; Wireless ad hoc network; Mobile device; Distributed computing; Real-time computing; Wireless; Mathematical optimization; Computer network; 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":[0.0003659155,0.000432295,0.0005085225,0.0002491436,0.0004483965,0.000219307,0.000859948,0.0002288407,0.000009849128],"category_scores_gemma":[0.000007947084,0.000477004,0.0002973418,0.001366179,0.00006412671,0.000154586,0.00002242012,0.000590218,0.00001277859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178511,"about_ca_system_score_gemma":0.00006925304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003899227,"about_ca_topic_score_gemma":0.00001296858,"domain_scores_codex":[0.9966131,0.0001786558,0.0007281398,0.00115892,0.000409428,0.000911777],"domain_scores_gemma":[0.9979163,0.0008844223,0.0002035802,0.0005864006,0.0001131863,0.0002960662],"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":[0.00002874454,0.0002246613,0.00000450864,0.00001808327,0.00002256255,0.00002091578,0.0004606795,0.8302749,0.0002621764,0.0002254302,0.00001267361,0.1684446],"study_design_scores_gemma":[0.001133689,0.0004036658,0.000006351772,0.0001022347,0.00001319685,0.00001249641,0.00008467173,0.9919741,0.004867739,0.000009392852,0.000913545,0.000478888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05904834,0.0003288131,0.937638,0.0001449621,0.001540541,0.000667054,0.000008062163,0.0005581355,0.00006612541],"genre_scores_gemma":[0.9263591,0.00005940991,0.07216749,0.0007826159,0.0003353491,0.0001977115,0.000005523373,0.00006295992,0.0000298807],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8673107,"threshold_uncertainty_score":0.9997681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731951000678403,"score_gpt":0.2455465922750181,"score_spread":0.228227082268234,"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."}}