Communication-Estimation Tradeoffs in Wireless Sensor Networks

Abstract

The distributed nature of wireless sensor networks illustrates well classical engineering tradeoffs: how to minimize communication (and possibly computation) cost, and thus energy dissipation, while maintaining acceptable performance levels in estimation and inference applications. We study a simple sensor network under dependent Gaussian noise and develop strategies for parameter estimation in a variety of communication scenarios. From an energy point of view, sending all data to a fusion center is the most costly, but leads to optimum performance results. Processing data at each sensor and sending parameter estimates and associated quality measures is a reasonable communication saving procedure and yet, in some cases, may lead to performance equivalent to sending all data to the fusion center. A sequential procedure is most parsimonious in terms of communication cost and especially effective in large wireless sensor networks. We explore those conditions for which little, or no loss in performance is encountered with this sequential procedure. Specifically, we provide analytical expressions for the maximum likelihood estimator under "geometric" dependent noise. We show, by means of analysis and simulations, that the performance is only marginally degraded when the noise is assumed to be independent.

Dimensionality Reduction and Noise Removal in Wireless Sensor Networks

“…Several efforts have been made to find the optimal balance between which parameters, where, and how to remove them. For the most part, experts in the field agree that it is more beneficial to remove noise and/or compress data at the node level [1][2][3]. This is mainly stressed so that the low power, low bandwidth, and low computational overhead of the wireless sensor network node constraints are met while fused datasets can still be used to make reliable decisions [4][5][6].…”

A Coalitional Game for Distributed Inference in Sensor Networks With Dependent Observations

“…Sundaresan et al [5] considered location estimation of a random signal source where they focused on improving system performance by exploiting the spatial dependence of sensor observations. Parameter estimation with dependent observations in a variety of communication scenarios was considered in [12], but was limited to the case of "geometric" dependent Gaussian noise.…”

An Algorithm for Denoising and Compression in Wireless Sensor Networks

“…Several efforts have been made to find the optimal balance between which parameters, where, and how to remove them. For the most part, experts in the field agree that it is more beneficial to remove noise and/or compress data at the node level [Closas, P., 2007], [Yamamoto, H., 2005], [Son, S.-H., 2005]. This is mainly stressed so that the low power, low bandwidth, and low computational overhead of the wireless sensor network node constraints are met while fused datasets can still be used to make reliable decisions [Abdallah, A., 2006], [Schizas, I.D., 2006], [Pescosolido, L., 2008].…”