Monte Carlo optimization of decentralized estimation networks over directed acyclic graphs under communication constraints

Üney, Murat and Çetin, Müjdat (2011) Monte Carlo optimization of decentralized estimation networks over directed acyclic graphs under communication constraints. IEEE Transactions on Signal Processing, 59 (11). pp. 5558-5576. ISSN 1053-587X

This is the latest version of this item.

[thumbnail of uney_TSP11.pdf] PDF
Restricted to Registered users only

Download (5MB) | Request a copy


Motivated by the vision of sensor networks, we consider decentralized estimation networks over bandwidth–limited communication links, and are particularly interested in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We employ a class of in–network processing strategies that admits directed acyclic graph representations and yields a tractable Bayesian risk that comprises the cost of communications and estimation error penalty. This perspective captures a broad range of possibilities for processing under network constraints and enables a rigorous design problem in the form of constrained optimization. A similar scheme and the structures exhibited by the solutions have been previously studied in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt this framework for estimation, however, the corresponding optimization scheme involves integral operators that cannot be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain particle representations and approximate computational schemes for both the in–network processing strategies and their optimization. The proposed Monte Carlo optimization procedure operates in a scalable and efficient fashion and, owing to the non-parametric nature, can produce results for any distributions provided that samples can be produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically as the communication becomes more costly, through a parameterized Bayesian risk.
Item Type: Article
Uncontrolled Keywords: Decentralized estimation, communication constrained inference, random fields, message passing algorithms, graphical models, Monte Carlo methods, wireless sensor networks, in–network processing
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Müjdat Çetin
Date Deposited: 02 Jan 2012 22:39
Last Modified: 26 Apr 2022 08:52

Available Versions of this Item

Actions (login required)

View Item
View Item