{"id":"W1981377771","doi":"10.1155/2007/56592","title":"A Cross-Layer Optimization Approach for Energy Efficient Wireless Sensor Networks: Coalition-Aided Data Aggregation, Cooperative Communication, and Energy Balancing","year":2007,"lang":"en","type":"article","venue":"Advances in Multimedia","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wireless sensor network; Computer network; Computer science; Efficient energy use; Sink (geography); Energy consumption; Data aggregator; Cross-layer optimization; Data transmission; Node (physics); Key distribution in wireless sensor networks; Transmission (telecommunications); Distributed computing; Wireless; Wireless network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002194358,0.0014209,0.001023972,0.0007410895,0.0005084722,0.001422261,0.001524264,0.001406619,0.001565935],"category_scores_gemma":[0.002395415,0.0004843581,0.0010867,0.001190461,0.0009803846,0.001739343,0.001761472,0.001278352,0.0003224845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018055,"about_ca_system_score_gemma":0.001033008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001612656,"about_ca_topic_score_gemma":0.001047887,"domain_scores_codex":[0.9990564,0.0004212121,0.00003784721,0.0001067005,0.0002986692,0.0000790813],"domain_scores_gemma":[0.9994283,0.0002785445,0.00008576096,0.00005118531,0.0001285999,0.00002756099],"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":[0.00002454204,0.0000539449,0.0002377402,0.00008901469,0.00008247978,0.00008166204,0.0000524556,0.935398,0.001928539,0.04457247,0.0008191816,0.01665993],"study_design_scores_gemma":[0.000006098812,0.00002962561,0.00004546523,0.00000561888,0.00001169726,0.00001882853,0.000006911439,0.991748,0.0004146615,0.006906837,0.0008002251,0.000005973565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004066277,0.0007413504,0.9920854,0.0003114231,0.00006766065,0.00003501706,0.00001345261,0.0000400578,0.002639302],"genre_scores_gemma":[0.5159456,0.003129782,0.4716235,0.0006857967,0.0004250968,0.0006856061,0.0001153218,0.0001449502,0.007244362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002194358,"threshold_uncertainty_score":0.01160502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02860116680577074,"score_gpt":0.314736558873868,"score_spread":0.2861353920680973,"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."}}