TY - GEN

T1 - Controlled collaboration for linear coherent estimation in wireless sensor networks

AU - Kar, Swarnendu

AU - Varshney, Pramod K.

PY - 2012

Y1 - 2012

N2 - We consider a wireless sensor network consisting of multiple nodes that are coordinated by a fusion center (FC) in order to estimate a common signal of interest. In addition to being coordinated, the sensors are also able to collaborate, i.e., share observations with other neighboring nodes, prior to transmission. In an earlier work, we derived the energy-optimal collaboration strategy for the single-snapshot framework, where the inference has to be made based on observations collected at one particular instant. In this paper, we make two important contributions. Firstly, for the single-snapshot framework, we gain further insights into partially connected collaboration networks (nearest-neighbor and random geometric graphs for example) through the analysis of a family of topologies with regular structure. Secondly, we explore the estimation problem by adding the dimension of time, where the goal is to estimate a time-varying signal in a power-constrained network. To model the time dynamics, we consider the stationary Gaussian process with exponential covariance (sometimes referred to as Ornstein-Uhlenbeck process) as our representative signal. For such a signal, we show that it is always beneficial to sample as frequently as possible, despite the fact that the samples get increasingly noisy due to the power-constrained nature of the problem. Simulation results are presented to corroborate our analytical results.

AB - We consider a wireless sensor network consisting of multiple nodes that are coordinated by a fusion center (FC) in order to estimate a common signal of interest. In addition to being coordinated, the sensors are also able to collaborate, i.e., share observations with other neighboring nodes, prior to transmission. In an earlier work, we derived the energy-optimal collaboration strategy for the single-snapshot framework, where the inference has to be made based on observations collected at one particular instant. In this paper, we make two important contributions. Firstly, for the single-snapshot framework, we gain further insights into partially connected collaboration networks (nearest-neighbor and random geometric graphs for example) through the analysis of a family of topologies with regular structure. Secondly, we explore the estimation problem by adding the dimension of time, where the goal is to estimate a time-varying signal in a power-constrained network. To model the time dynamics, we consider the stationary Gaussian process with exponential covariance (sometimes referred to as Ornstein-Uhlenbeck process) as our representative signal. For such a signal, we show that it is always beneficial to sample as frequently as possible, despite the fact that the samples get increasingly noisy due to the power-constrained nature of the problem. Simulation results are presented to corroborate our analytical results.

UR - http://www.scopus.com/inward/record.url?scp=84875694055&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84875694055&partnerID=8YFLogxK

U2 - 10.1109/Allerton.2012.6483237

DO - 10.1109/Allerton.2012.6483237

M3 - Conference contribution

AN - SCOPUS:84875694055

SN - 9781467345385

T3 - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012

SP - 334

EP - 341

BT - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012

T2 - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012

Y2 - 1 October 2012 through 5 October 2012

ER -