Abstract
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 together with a non-linear consensus algorithm is introduced herein. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives a guideline towards how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded transmit function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.
| Original language | English (US) |
|---|---|
| Article number | 7574375 |
| Journal | IEEE Sensors Journal |
| Volume | PP |
| Issue number | 99 |
| DOIs | |
| State | Published - 2016 |
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Keywords
- Adaptive Step Size
- Asymptotic Covariance
- Bounded Transmissions
- Max Consensus
- Soft-max
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering
Cite this
Max Consensus in Sensor Networks : Non-linear Bounded Transmission and Additive Noise. / Zhang, Sai; Tepedelenlioglu, Cihan; Banavar, Mahesh K.; Spanias, Andreas.
In: IEEE Sensors Journal, Vol. PP, No. 99, 7574375, 2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Max Consensus in Sensor Networks
T2 - Non-linear Bounded Transmission and Additive Noise
AU - Zhang, Sai
AU - Tepedelenlioglu, Cihan
AU - Banavar, Mahesh K.
AU - Spanias, Andreas
PY - 2016
Y1 - 2016
N2 - 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 together with a non-linear consensus algorithm is introduced herein. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives a guideline towards how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded transmit function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.
AB - 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 together with a non-linear consensus algorithm is introduced herein. A design parameter controls the trade-off between the soft-max error and convergence speed. An analysis of this trade-off gives a guideline towards how to choose the design parameter for the max estimate. We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm. A shifted non-linear bounded transmit function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements. Simulation results corroborating the theory are also provided.
KW - Adaptive Step Size
KW - Asymptotic Covariance
KW - Bounded Transmissions
KW - Max Consensus
KW - Soft-max
UR - http://www.scopus.com/inward/record.url?scp=85026996841&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026996841&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2016.2612642
DO - 10.1109/JSEN.2016.2612642
M3 - Article
AN - SCOPUS:85026996841
VL - PP
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
IS - 99
M1 - 7574375
ER -