Overview
We aim at developing a wide
spectrum of modeling methodologies and related protocols for large-scale
embedded systems under realistic environments. The main objective of this
project lies in developing three novel modeling approaches, which complement
each other and cover a large cost-benefit design space. The first is to develop
a new radio irregularity model based on concepts of degree of irregularity and
variance of signaling power. The second provides abstraction of a completely
repeatable physical environment, using an automated capture-and-replay process.
The third features a novel way to use training events in a controlled manner to
produce non-parametric realistic sensing and communication patterns. A key
challenge for in-situ modeling lies in reconciling the conflict between the
in-situ modeling accuracy and the related cost to build and use these models in
resource-limited large-scale embedded systems. We develop the models from the
micro to macro levels where designers can choose the appropriate level of detail
based on the accuracy needs, and also the models from parametric to
non-parametric types where designers can choose the models with proper costs
based on the available resources. The models will be available via common
simulation systems, enabling embedded systems designers to develop solutions
based on realism and avoid an all-to-common problem found today where solutions
developed by simulation don't work in the real world. This, in turn, will have a
major impact on embedded systems by saving development time and money and
resulting in more efficient, robust, predictable and controllable systems.
Research
Directions
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In this work, we investigate the impact of radio irregularity on the
communication performance in wireless sensor networks. With empirical
data
obtained from the MICA2 platform, we establish a radio model for
simulation, called the Radio Irregularity Model (RIM). This model is the
first to bridge the discrepancy between spherical radio models used by
simulators and the physical reality of radio signals. With this model,
we are able to analyze the impact of radio irregularity on some of the
well-known MAC and routing protocols. |
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In this project, we design and
implement a practical Sensing Area Modeling technique, called SAM. By
injecting controlled events through regular and hierarchical training,
SAM estimates the sensing areas of individual sensor nodes accurately.
We also propose several model abstraction techniques to concisely define
the sensing areas with a small loss of accuracy. This work is the
first to investigate the impact of irregular sensing area on application
performance, such as coverage scheduling and tracking. We evaluate SAM
using theoretical analysis, a physical experiment on a testbed
consisting of 40 MicaZ motes, as well as an extensive 1000- node
simulation. Our evaluation results reveal serious problems caused by
circular sensing model, while demonstrating significant performance
improvements when SAM is used. |
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In this work, we design and implement a lightweight algorithm of
Adaptive Transmission Power Control for wireless sensor networks. In
ATPC, each node builds a model for each of its neighbors, describing the
correlation between transmission power and link quality. With this
model, we employ a feedback-based transmission power control algorithm
to dynamically maintain individual link quality over time. The
intellectual contribution of this work lies in a novel pairwise
transmission power control, which is significantly different from
existing node-level or network-level power control methods. |
This Page was last modified by
11/18/2006
Authors:
Tian He
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