Abstract
A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of 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 algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings.
| Original language | English (US) |
|---|---|
| Article number | 6605593 |
| Pages (from-to) | 6000-6009 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Signal Processing |
| Volume | 61 |
| Issue number | 23 |
| DOIs | |
| State | Published - 2013 |
Fingerprint
Keywords
- Asymptotic Covariance
- Bounded Transmissions
- Distributed Consensus
- Markov Processes
- Sensor Networks
- Stochastic Approximation
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
Cite this
Non-linear distributed average consensus using bounded transmissions. / Dasarathan, Sivaraman; Tepedelenliolu, Cihan; Banavar, Mahesh K.; Spanias, Andreas.
In: IEEE Transactions on Signal Processing, Vol. 61, No. 23, 6605593, 2013, p. 6000-6009.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Non-linear distributed average consensus using bounded transmissions
AU - Dasarathan, Sivaraman
AU - Tepedelenliolu, Cihan
AU - Banavar, Mahesh K.
AU - Spanias, Andreas
PY - 2013
Y1 - 2013
N2 - A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of 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 algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings.
AB - A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of 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 algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings.
KW - Asymptotic Covariance
KW - Bounded Transmissions
KW - Distributed Consensus
KW - Markov Processes
KW - Sensor Networks
KW - Stochastic Approximation
UR - http://www.scopus.com/inward/record.url?scp=84888117047&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84888117047&partnerID=8YFLogxK
U2 - 10.1109/TSP.2013.2282912
DO - 10.1109/TSP.2013.2282912
M3 - Article
AN - SCOPUS:84888117047
VL - 61
SP - 6000
EP - 6009
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 23
M1 - 6605593
ER -