Networked and Embedded Systems Laboratory

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Sensors and devices used in wireless sensor networks are state-of-the-art technology
with the lowest possible price. The sensor measurements we get from
these devices are therefore often noisy, incomplete and inaccurate. Researchers
studying wireless sensor networks hypothesize that much more information can
be extracted from hundreds of unreliable measurements spread across a field of
interest than from a smaller number of high-quality, high-reliability instruments
with the same total cost. This thesis offers a basis for exploring that hypothesis
in some detail. We make four contributions. First, we describe sensor faults
commonly seen in recent sensor network deployments, and we formulate statistical
models to assist in the analysis of those faults. Second, we present some
basic tools for assessing the robustness of aggregation algorithms to these common
faults. We then address, in two separate ways, the issue of finding linear
calibration parameters while sensors are deployed. Our third contribution is an
approach to calibration using state space models and non-linear, non-Gaussian
filtering techniques to calibrate sensors without groundtruth knowledge or controlled
stimuli. We evaluate this calibration on simulated sensor data with a
simple dynamical model based on the physical process of soil moisture. Fourth,
we present a general problem formulation for blind calibration which assumes
that the n sensor measurements lie in a subspace of n-dimensional space. We
prove the identifiability of the sensor offsets and gains under this assumption,
and we evaluate implementations on both simulated and real sensor data.

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