2010/12/21 by Dragana Bajović, Dragana Bajovic, Dušan Jakovetić +11
Computer Science · Mathematics · #Distributed Control Multi-Agent Systems #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Target Tracking and Data Fusion in Sensor Networks #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1012.4668
30 pages, journal, submitted on December 3rd, 2010
arxiv created 2010/12/21 · openalex publication_date 2010/12/21 · arxiv updated 2010/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the large deviations performance, i.e., the exponential decay rate of the error probability, of distributed detection algorithms over random networks. At each time step k each sensor: 1) averages its decision variable with the neighbors' decision variables; and 2) accounts on-the-fly for its new observation. We show that distributed detection exhibits a "phase change" behavior. When the rate of network information flow (the speed of averaging) is above a threshold, then distributed detection is asymptotically equivalent to the optimal centralized detection, i.e., the exponential decay rate of the error probability for distributed detection equals the Chernoff information. When the rate of information flow is below a threshold, distributed detection achieves only a fraction of the Chernoff information rate; we quantify this achievable rate as a function of the network rate of information flow. Simulation examples demonstrate our theoretical findings on the behavior of distributed detection over random networks.