Belief consensus and distributed hypothesis testing in sensor networks

By Reza Olfati-Saber, Elisa Franco, Emilio Frazzoli, Jeff S. Shamma. 2006

193 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Peer-to-peer Federated Learning on Graphs (2019, background): “…been studied in statistics, economics, and operations research and encompasses canonical problems of consensus DeGroot (1974), belief propagation (Olfati-Saber et al., 2005), and distributed hypothesis testing (Jad-babaie et al., 2013; Nedi ´ c et al., 2015; Shahrampour et al., 2016; Lalitha et…”
Defending non-Bayesian learning against adversarial attacks (2016, positive): “Comparing to the algorithm in [20], one advantage of the learning rule in [4] is that it incorporates the newly obtained private signals into learning on the fly.”
Information Weighted Consensus Filters and Their Application in Distributed Camera Networks (2013, background): “For example, the item being estimated may be the arithmetic mean (average consensus) [3] or the geometric mean [8] of the initial values.”
Distributed algorithms for reaching consensus on general functions (2008, background): “These include average consensus [25, 26, 30], averagemax-min consensus [8], geometric-mean consensus [24], and power-mean consensus [3].”
Consensus and Cooperation in Networked Multi-Agent Systems (2007, methodology): “There are a variety of functions that can be computed in a similar fashion using synchronous or asynchronous distributed algorithms (see [10], [28], [30], [ 73 ], and [76]).”
Algebraic Connectivity Ratio of Ramanujan Graphs (2007, methodology): “Nearest-neighbors graphs arise in consensus-based information fusion in ad hoc sensor networks [26], [4], [16], [23], distributed hypothesis testing using belief propagation [21], and proximity graphs in flocking theory [19].”
Consensus in multi-agent systems: a review (2021, background): “Consensus means the interaction between groups of agents in a team, via sensors or a communication network, to reach an agreement on a common value or state (Olfati-Saber and Shamma 2005; Schenato and Gamba 2007; Olfati-Saber et al. 2006).”
Social learning and distributed hypothesis testing (2014, background): “Jadbabaie et al. [1] provide an excellent overview of the large body of literature on distributed estimation, detection, hypothesis testing and learning over social networks; we discuss those most relevant to this paper [1]–[5] in this section.”
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters (2018, background): “, the data is produced by a distributed network of sensors [35, 33, 32] or the transmission of information is limited by communication or privacy constraints, i.”
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms (2009, background): “, 2007), applications to distributed signal processing (Spanos et al., 2005; Xiao et al., 2005; Olfati-Saber et al., 2006; Zhu and Mart́ınez, 2008a), characterization of convergence rates and time complexity (Landau and Odlyzko, 1981; Olshevsky and Tsitsiklis, 2009; Cao et al.”
Distributed Kalman Filter with Embedded Consensus Filters (2005, background): “Sensor networks and intelligent arrays of micro-sensors have broad range of applications including information gathering and data fusion for modeling an environment, surveillance, active monitoring of forests & agricultural lands, health-care applications, collaborative information processing, and c”
Consensus Filters for Sensor Networks and Distributed Sensor Fusion (2005, background): “Sensor networks have broad applications in surveillance & monitoring of an environment, collaborative processing of information, and gathering scientific data from spatially distributed sources for environmental modeling and protection [19], [4], [8], [10], [2], [1], [3], [13], [14], [17], [23], [25”