Consensus Algorithms and Distributed Structure Estimation in Wireless Sensor Networks

Published on Jan 1, 2017
Sai Zhang7
Estimated H-index: 7
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Abstract
馃摉 Papers frequently viewed together
2010
1 Author (Steve Saed)
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References63
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#1Sai Zhang (ASU: Arizona State University)H-Index: 7
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
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Distributed node counting in wireless sensor networks can be important in various applications, such as network maintenance and information aggregation. In this paper, a distributed consensus algorithm for estimating the number of nodes in a wireless sensor network in the presence of communication noise is introduced. In networks with a fusion center, counting the number of nodes can easily be done by letting each node to transmit a fixed constant value to the fusion center. In a network without...
12 CitationsSource
#1Sai Zhang (ASU: Arizona State University)H-Index: 7
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
view all 4 authors...
A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor. In the presence of communication noise, however, the maximum estimate will incorrectly drift and the estimate at each sensor will diverge. As a result, a soft-max approximation to...
27 CitationsSource
Dec 1, 2016 in GLOBECOM (Global Communications Conference)
#1Sai Zhang (ASU: Arizona State University)H-Index: 7
#2Jongmin Lee (ASU: Arizona State University)H-Index: 6
Last. Andreas SpaniasH-Index: 32
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A distributed consensus algorithm for estimating the degree distribution of a graph is proposed. The proposed algorithm is based on average consensus and in-network empirical mass function estimation. It is fully distributed in the sense that each node in the network only needs to know its own degree, and nodes do not need to be labeled. The algorithm works for any connected graph structure in the presence of communication noise. The performance of the algorithm is analyzed. A discussion on how ...
3 CitationsSource
#1Sai Zhang (ASU: Arizona State University)H-Index: 7
Last. Andreas SpaniasH-Index: 32
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System size estimation in distributed wireless sensor networks is important in various applications such as network management and maintenance. One popular method for system size estimation is to use distributed consensus algorithms with randomly generated initial values at nodes. In this paper, the performance of such methods is studied and Fisher information and Cramer-Rao bounds (CRBs) for different consensus algorithms are derived. Errors caused by communication noise and lack of convergence...
3 CitationsSource
This paper deals with the analysis of the convergence properties of the max-consensus protocol in presence of asynchronous updates and bounded time delays on directed static networks. The work is motivated by real-world applications in distributed decision-making systems, for which max-consensus is an effective paradigm. The main result of this paper is that the strongly connectedness of the directed communication network is a sufficient condition for the asynchronous max-consensus protocol to l...
26 CitationsSource
#1Sivaraman Dasarathan (ASU: Arizona State University)H-Index: 4
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
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A distributed average consensus algorithm robust to a wide range of impulsive channel noise distributions is proposed. This work is the first of its kind in the literature to propose a consensus algorithm which relaxes the requirement of finite moments on the communication noise. It is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algo...
13 CitationsSource
#1Mahesh K. Banavar (Clarkson University)H-Index: 17
#2Jun Jason Zhang (DU: University of Denver)H-Index: 17
Last. Antonia Papandreou-Suppappola (ASU: Arizona State University)H-Index: 24
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We provide an overview of recent work on distributed and agile sensing algorithms and their implementation. Modern sensor systems with embedded processing can allow for distributed sensing to continuously infer intelligent information as well as for agile sensing to configure systems in order to maintain a desirable performance level. We examine distributed inference techniques for detection and estimation at the fusion center and wireless networks for the sensor systems for real time scenarios....
15 CitationsSource
#1Sivaraman Dasarathan (ASU: Arizona State University)H-Index: 4
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
A distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel is considered. When the sensor measurements are decreasingly reliable as a function of the sensor index, the conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided th...
10 CitationsSource
#1Damiano VaragnoloH-Index: 17
#2Gianluigi Pillonetto (UNIPD: University of Padua)H-Index: 30
Last. Luca Schenato (UNIPD: University of Padua)H-Index: 50
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We consider estimation of network cardinality by distributed anonymous strategies relying on statistical inference methods. In particular, we focus on the relative Mean Square Error (MSE) of Maximum Likelihood (ML) estimators based on either the maximum or the average of M-dimensional vectors randomly generated at each node. In the case of continuous probability distributions, we show that the relative MSE achieved by the max-based strategy decreases as 1/M, independently of the used distributio...
40 CitationsSource
Dec 1, 2013 in CDC (Conference on Decision and Control)
#1H氓kan TereliusH-Index: 6
#2Damiano VaragnoloH-Index: 17
Last. Karl Henrik JohanssonH-Index: 92
view all 4 authors...
The aggregation and estimation of values over networks is fundamental for distributed applications, such as wireless sensor networks. Estimating the average, minimal and maximal values has already been extensively studied in the literature. In this paper, we focus on estimating empirical distributions of values in a network with anonymous agents. In particular, we compare two different estimation strategies in terms of their convergence speed, accuracy and communication costs. The first strategy...
6 CitationsSource
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Oct 1, 2017 in ASILOMAR (Asilomar Conference on Signals, Systems and Computers)
#1Sai Zhang (ASU: Arizona State University)H-Index: 7
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 28
Last. Andreas Spanias (ASU: Arizona State University)H-Index: 32
view all 3 authors...
A fully distributed algorithm for estimating the center and coverage region of a wireless sensor network (WSN) is proposed. The proposed algorithm is useful in many applications, such as finding the required power for a certain level of connectivity in WSNs and localizing a service center in a network. The network coverage region is defined to be the smallest sphere that covers all the sensor nodes. The center and radius of the smallest covering sphere are estimated. The center estimation is for...
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