{"id":"W2104780561","doi":"10.1109/icc.2009.5198997","title":"Energy-Efficient Itinerary Planning for Mobile Agents in Wireless Sensor Networks","year":2009,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wireless sensor network; Computer science; Efficient energy use; Distributed computing; Data aggregator; Computation; Data collection; Selection (genetic algorithm); Mobile agent; Energy (signal processing); Open research; Computer network; Wireless; Real-time computing; Algorithm; Machine learning; Telecommunications; Engineering; Mathematics","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":[0.001373396,0.0007625344,0.0007282912,0.0007456373,0.0007862239,0.000843639,0.001460364,0.0008615523,0.0009460507],"category_scores_gemma":[0.003640835,0.0005078689,0.0005835501,0.001120002,0.0008027986,0.001847601,0.001207979,0.0008955619,0.0002107984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007335522,"about_ca_system_score_gemma":0.001059815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002356646,"about_ca_topic_score_gemma":0.003503039,"domain_scores_codex":[0.9994484,0.000220188,0.00003553688,0.0001038214,0.000110281,0.00008179444],"domain_scores_gemma":[0.9988832,0.00068373,0.0001551709,0.00009615174,0.000109026,0.00007265995],"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":[0.000125404,0.00005409102,0.0005900405,0.00006232598,0.00003425058,0.00007824426,0.000162016,0.9239867,0.002934003,0.02460666,0.0006199333,0.04674637],"study_design_scores_gemma":[0.00002044584,0.00005653018,0.0001114949,0.000007128431,0.00001150718,0.00003213993,0.00003879161,0.9843482,0.00121069,0.01343771,0.0007162627,0.00000896409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02998996,0.0003852502,0.9673334,0.0001860136,0.00002007369,0.00005293098,0.0000226262,0.0001880979,0.001821699],"genre_scores_gemma":[0.7523709,0.0004509223,0.2446736,0.0000765499,0.00002709989,0.0002046517,0.00009908457,0.00007862236,0.002018767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002356646,"threshold_uncertainty_score":0.007263303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01937662031481332,"score_gpt":0.2635285252576761,"score_spread":0.2441519049428628,"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."}}