{"id":"W1969520210","doi":"10.1109/icif.2006.301722","title":"Efficient Control of Information Flow for Distributed Multisensor Fusion Using Markov Decision Processes","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Sensor fusion; Redundancy (engineering); Markov decision process; Distributed computing; Process (computing); Information flow; Markov process; Information exchange; Decentralised system; Distributed database; Data mining; Real-time computing; Control (management); Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0002576121,0.0001652507,0.0002317075,0.0001668615,0.0001542978,0.0001041292,0.0003941901,0.00009890119,0.000005184836],"category_scores_gemma":[0.000188843,0.0001371516,0.00008080849,0.0006591165,0.00003925432,0.0002852738,0.00009527666,0.00005386939,0.000003557554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006848462,"about_ca_system_score_gemma":0.00007105436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006157379,"about_ca_topic_score_gemma":0.00001710461,"domain_scores_codex":[0.9984401,0.00003090334,0.0005620117,0.0002407572,0.0004084832,0.0003177539],"domain_scores_gemma":[0.9981592,0.0004956823,0.0002805442,0.0003504636,0.0006590449,0.000055027],"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":[0.00006073555,0.00009874893,0.000108943,0.00004803861,0.000004299094,5.699124e-7,0.00002822491,0.9777647,0.001075199,0.00236197,0.0001852428,0.01826327],"study_design_scores_gemma":[0.001480493,0.00004933934,0.000474193,0.0000757443,0.00001009505,0.000004674257,0.000009198019,0.9906835,0.006393114,0.0001219859,0.0005345703,0.0001630675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1330805,0.00007028985,0.8658507,0.00005156738,0.0002516977,0.0004295785,0.00004367435,0.0001309916,0.00009095806],"genre_scores_gemma":[0.6257664,0.00000205123,0.374107,0.0000321651,0.00003318167,0.000009706598,0.00003733997,0.000005385117,0.000006740018],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4926859,"threshold_uncertainty_score":0.5592878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00677401962886057,"score_gpt":0.2190032483223722,"score_spread":0.2122292286935116,"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."}}