In the last decade there has been an increase in application areas for wireless sensor
networks (WSNs), which can be attributed to the advances in the enabling sensor technology.
These advances include integrated circuit miniaturization and mass-production of
highly-reliable hardware for sensing, processing, and data storage at a lower cost. In many
emerging applications, massive amounts of data are acquired by a large number of low-cost
sensing devices. The design of signal processing algorithms for these WSNs, unlike in wireless
networks designed for communications, face a different set of challenges due to resource
constraints sensor nodes must adhere to. These include: (i) limited on-board memory for
storage; (ii) limited energy source, typically based on irreplaceable battery cells; (iii) radios
with limited transmission range; and (iv) stringent data rates either due to a need to save
energy or due to limited radio-frequency bandwidth allocated to sensor networks.
This work addresses distributed data-reduction at sensor nodes using a combination
of measurement-censoring and measurement quantization. The WSN is envisioned for decentralized
estimation of either a vector of unknown parameters in a maximum likelihood
framework, or, for decentralized estimation of a random signal using Bayesian optimality
criteria. Early research effort in data-reduction methods involved using a centralized
computation platform directing selection of the most informative data and focusing computational
and communication resources toward the selected data only. Robustness against
failure of the central computation unit, as well as the need for iterative data-selection and
data-gathering in some applications (e.g., real-time navigation systems), motivates a rethinking
of the centralized data-selection approach. Recently, research focus has been on
collaborative signal processing in sensor neighborhoods for the data-reduction step. It is in
this spirit that investigation of methods for sensor node-based data reduction is pursued,
where each sensor decides whether and what to communicate.
The scope of this dissertation encompasses distributed algorithms for the measurementreduction
step and algorithms for estimation which are amenable to either in-network or
fusion center-based implementation using a WSN. Clearly-defined optimality criteria are
used as the foundation for development of algorithms for data reduction and estimation.
Performance analysis is provided and corroborated using simulated and real-world test cases
illustrating the potential of the novel methods.
University of Minnesota Ph.D. dissertation. July 2011. Major: Electrical Engineering. Advisor: Professor Georgios B. Giannakis. 1 computer file (PDF); xi, 109 pages, appendices A.C.
Msechu, Eric James.
Estimation with wireless sensor networks:censoring and quantization perspectives..
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