NDLI: Distributed Field Estimation With Randomly Deployed, Noisy, Binary Sensors

Content Provider	IEEE Xplore Digital Library
Author	Ye Wang Ishwar, P.
Copyright Year	1991
Abstract	The reconstruction of a bounded deterministic field from binary-quantized observations of sensors which are randomly deployed over the field domain is studied. The sensor observations are corrupted by bounded additive noise. The study focuses on the extremes of lack of deterministic control in the sensor deployment, lack of knowledge of the noise distribution, and lack of sensing precision and reliability. Such adverse conditions are motivated by possible real-world scenarios where a large collection of low-cost, crudely manufactured sensors are mass-deployed in an environment where little can be assumed about the ambient noise. A simple estimator that reconstructs the entire field from these unreliable, binary-quantized, noisy observations is proposed. Technical conditions for the almost sure and mean squared error (MSE) convergence of the estimate to the field, as the number of sensors tends to infinity, are derived and their implications are discussed. For finite-dimensional, bounded-variation, and Sobolev-differentiable function classes, specific MSE decay rates are derived.
Sponsorship	IEEE Signal Processing Society
Starting Page	1177
Ending Page	1189
Page Count	13
File Size	434026
File Format	PDF
ISSN	1053587X
Volume Number	57
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Convergence Working environment noise Minimax techniques Monte Carlo methods Upper bound Sufficient conditions Additive noise Manufacturing H infinity control Source coding sensor networks Almost sure convergence distributed source coding dithered scalar quantization minimax rate of convergence Monte Carlo sampling non-parametric field regression oversampled analog-to-digital conversion scaling law
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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