Energy scaling laws for distributed inference in random fusion networks

IEEE Journal on Selected Areas in Communications

Volumn 27, Issue 7, 2009, Pages 1203-1217

  Anandkumar, Animashree ^a   Yukich, Joseph E ^b   Tong, Lang ^a   Swami, Ananthram ^c  

^a Cornell University   (United States)

^b LEHIGH UNIVERSITY   (United States)

^c U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Distributed inference; Euclidean random graphs; Graphical models; Stochastic geometry and data fusion

Indexed keywords

ASYMPTOTIC ENERGY; AVERAGE ENERGY; DATA FUSION NETWORKS; DEPENDENCY GRAPHS; DISTRIBUTED INFERENCE; ENERGY SCALING LAW; EUCLIDEAN; FUSION CENTER; GRAPHICAL MODEL; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; MINIMUM SPANNING TREES; MULTIHOP; SENSOR MEASUREMENTS; STATISTICAL INFERENCE; STOCHASTIC GEOMETRY;

ENERGY POLICY; ENERGY UTILIZATION; IMAGE SEGMENTATION; OPTIMIZATION; SCALING LAWS; SENSORS; STOCHASTIC MODELS; TREES (MATHEMATICS);

SENSOR DATA FUSION;

EID: 79960877404     PISSN: 07338716     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSAC.2009.090916     Document Type: Article

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