{"id":"W4323654392","doi":"10.18280/isi.280127","title":"Energy Efficient Routing Algorithm for WSN-IoT Network","year":2023,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Routing algorithm; Wireless sensor network; Routing (electronic design automation); Hierarchical routing; Computer network; Internet of Things; Routing protocol; Energy (signal processing); Static routing; Algorithm; Distributed computing; Computer security; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001860981,0.0005535168,0.0005509891,0.0004763048,0.00127842,0.001311976,0.0009857196,0.0004915008,0.00002387557],"category_scores_gemma":[0.0002389145,0.0006281076,0.0003261357,0.003445254,0.0003252302,0.001828303,0.0005665996,0.0002990761,0.0003924141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006953761,"about_ca_system_score_gemma":0.0002736027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000377518,"about_ca_topic_score_gemma":0.00002993713,"domain_scores_codex":[0.995075,0.0002368871,0.001472955,0.0005234785,0.0007536103,0.001937999],"domain_scores_gemma":[0.9965771,0.0007226801,0.0008364402,0.0007778934,0.0007850965,0.0003008042],"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":[0.000006651434,0.00002092125,0.00001704218,0.000109709,0.00003717864,0.000006413578,0.001977442,0.4951521,0.000003456526,0.07115906,0.006079323,0.4254307],"study_design_scores_gemma":[0.0006025704,0.0001475704,0.0002338643,0.0007449676,0.00003521133,0.0000583711,0.0003053505,0.8940663,0.0002180117,0.002293555,0.1006911,0.0006030815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004435975,0.001517336,0.9761485,0.0005585618,0.01108783,0.0005427429,0.00006524261,0.001112973,0.004530876],"genre_scores_gemma":[0.6663423,0.0006699326,0.3122659,0.002180253,0.006100745,0.0006822289,0.001194971,0.0002056578,0.01035803],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6638826,"threshold_uncertainty_score":0.9997247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01930855960874558,"score_gpt":0.2338604679992076,"score_spread":0.214551908390462,"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."}}