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Sai Zhang
Arizona State University
13Publications
4H-index
36Citations
Publications 13
Newest
Published on Oct 1, 2018
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
+ 2 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
The analysis of a distributed consensus algorithm for estimating the maximum of the node initial state values in a network is considered in the presence of communication noise. Conventionally, the maximum is estimated by updating the node state value with the largest received measurements in every iteration at each node. However, due to additive channel noise, the estimate of the maximum at each node has a positive drift at each iteration and this results in nodes diverging from the true max val...
Published on Mar 2, 2018in Synthesis Lectures on Communications
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
+ 1 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
Abstract The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) g...
Published on Dec 1, 2017
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University)
+ 4 AuthorsRafaela Villalpando-Hernandez1
Estimated H-index: 1
A distributed spectral clustering algorithm to group sensors based on their location in a wireless sensor network (WSN) is proposed. For machine learning and data mining applications in WSN's, gathering data at a fusion center is vulnerable to attacks and creates data congestion. To avoid this, we propose a robust distributed clustering method without a fusion center. The algorithm combines distributed eigenvector computation and distributed K-means clustering. A distributed power iteration meth...
Published on Oct 1, 2017 in ASILOMAR (Asilomar Conference on Signals, Systems and Computers)
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
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...
Published on Feb 15, 2017in IEEE Sensors Journal 3.08
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
+ 1 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
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...
Published in IEEE Sensors Journal 3.08
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
+ -3 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
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...
Published on Dec 1, 2016 in GLOBECOM (Global Communications Conference)
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University),
Jongmin Lee3
Estimated H-index: 3
(ASU: Arizona State University)
+ 1 AuthorsAndreas Spanias25
Estimated H-index: 25
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 ...
Published on Sep 1, 2016
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
+ 2 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
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...
Published on Nov 1, 2015 in ASILOMAR (Asilomar Conference on Signals, Systems and Computers)
Sai Zhang4
Estimated H-index: 4
(ASU: Arizona State University),
Cihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
+ 1 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
A distributed consensus algorithm for estimating the number of nodes in a wireless sensor network in the presence of communication noise is proposed. The idea is based on estimating the norm of available samples at nodes. Each node generates its own random initial measurements and updates its state by only communicating with its neighbors: the algorithm is fully distributed with no assumptions about the structure of the network. We also show that there is a trade-off between the estimation error...
Published on Sep 1, 2014
Robert Santucci4
Estimated H-index: 4
(ASU: Arizona State University),
Mahesh K. Banavar12
Estimated H-index: 12
(ASU: Arizona State University)
+ 2 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
A preliminary investigation has been conducted into the use of orthogonal frequency-division multiple-access for distributed estimation. The key difference from previous work in the literature is that the channels between the sensors and the fusion center contain multiple paths with time lags, amplitudes, and phase rotations due to fading. Orthogonal frequency-division multiplexing has been proven to be an effective modulation scheme in the presence of multipath channels, and thus has been utili...
12