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\title{Sensor Networks for Structural Monitoring}
\author{{\bf Ananth Grama}}
\institution{
Parallel and Distributed Systems Lab\\
Department of Computer Sciences\\
Purdue University}
\slideCaption{}
\date{}

\begin{document}

\maketitle
\thispagestyle{empty}

\begin{slide}{Goal}
\begin{itemize}
\item {\RED
Designing sensing infrastructure for real-time, high fidelity
measurement}
\item Test infrastructure: three story $50'\times 30'$ structure
\item Physical phenomenon to be measured:
	\begin{itemize}
	\item Linear displacement
	\item Multi-axial acceleration
	\item Others...
	\end{itemize}
\end{itemize}
\end{slide}

\begin{slide}{Heterogenous Wireless Sensor Network}
\begin{itemize}
\item Hierarchical network
	\begin{itemize}
	\item Varying communication technologies: FM, Bluetooth, 802.11b
	\item Varying data rates: $\approx 30$Kbps to $>1Mbps$
	\item Varying CPU power: 8MHz (Atmega128 $\mu c$), 400MHz (XScale), 1-2GHz (Pentium)
	\end{itemize}
\end{itemize}
\includegraphics[width=0.35\textwidth,angle=-90]{motes-2.ps}
\includegraphics[width=0.1\textwidth,angle=-90]{sg-2.ps}
\includegraphics[width=0.35\textwidth,angle=-90]{laptop-pc.ps}
\end{slide}

\begin{slide}{Instrumentation: Displacement}
\begin{itemize}
\item Laser displacement sensor (Acuity AR600)
	\begin{itemize}
	\item Accuracy up to 0.04 inch
	\item Sampling rate up to 1.25KHz
	\end{itemize}
\item Laser sensors around the building accurately measure
building displacement in different axis
\item Higher sampling rates require an accompanying
network node with higher processing capability and memory
\centerline{\includegraphics[width=0.27\textwidth,angle=-90]{laser.ps}}
\end{itemize}
\end{slide}

\begin{slide}{Instrumentation: Acceleration}
\begin{itemize}
\item ADXL202 Micro-electro-mechanical
(MEMs) 2-D accelerometer on mica motes
	\begin{itemize}
	\item Range $+/- 2g$
	\item Sensitivity $12.5\%/g$, $2mg$ resolution at 60Hz
	\item Low power $< 0.6mA$ at $\sim 3V$
	\item Low cost
	\end{itemize}
\item Can use several motes dispersed in the building
\item Need to design efficient data processing software for motes
\item Need to correlate data from several sensors
\end{itemize}
\end{slide}

\begin{slide}[Replace]{System Architecture}
\begin{itemize}
\item Data centric distributed system architecture
\item {\RED Goal:} Allow engineers to macro-architect sensing infrastructure
\item Compose a distributed data processing application at run-time
from configurable software components
	\begin{itemize}
	\item Data processing at source
	\item Aggregation and in-network processing at hops
	\item Routing layer: push/pull data from source to sinks
	\end{itemize}
\end{itemize}
\centerline{$\ldots$ while meeting fidelity requirements}
\end{slide}

\begin{slide}{Data Processing at Source}
\begin{itemize}
\item Resource limitation: CPU, RAM
\item Need to minimize data transmission cost: constrained battery power
\item Signal processing: filter noise from data
\item Temporal correlation: compress data
\item Fourier analysis using an extremely lean integer-based FFT library
(currently under development).
\end{itemize}
\end{slide}

\begin{slide}{Data Processing at Source}
\begin{itemize}
\item Fourier analysis at data source:
\item Provides both filtering and compression
\item Separates software code from data semantics
	\begin{itemize}
	\item Allows building reusable software component
	\end{itemize}
\item Gives engineers control over data filtering and resolution
using run-time configurations
	\begin{itemize}
	\item Allows macro-architecting \& flexibility	
	\end{itemize}
\item {\RED Challenges:} Implementation on motes
	\begin{itemize}
	\item Need optimization due to CPU \& power limitation
	(8-bit, 8MHz, 4KB Data RAM, no FPU)
	\end{itemize}
\item Our immediate deliverable will be a TinyFFT library for the
motes. We expect to follow it with TinyKalman and other such signal
processing and aggregation methods.
\end{itemize}
\end{slide}

\begin{slide}{Aggregation \& In-Network Processing}
\begin{itemize}
\item Utilize spatial correlation to avoid congestion
on paths to sink
\item Leads to further noise filtering and compression
\item Makes redundant deployment of cheap nodes more feasible
\item Can combine multiple views of same physical phenomenon
	\begin{itemize}
	\item Infer larger trends
	\item Increase certainty in observations (Kalman filtering)
	\end{itemize}
\item Utilize powerful nodes as data traverses up the
hierarchy of nodes in the network
\end{itemize}
\end{slide}

\begin{slide}{Aggregation \& In-Network Processing}
\begin{itemize}
\item {\RED Challenges:}
	\begin{itemize}
	\item Lack of (or incomplete) spatial information of nodes
	\item Asynchrony amongst nodes
	(leading to phase shifts)
	\item Increase in computational requirement
	due to agregation of data
	\end{itemize}
\end{itemize}
\end{slide}

\begin{slide}{Routing}
\begin{itemize}
\item Routing data from $n$ sources to $m$ sinks
\item Exploit hierarchy in the network nodes
	\begin{itemize}
	\item Low diameter network
	\item Increased energy cost function optimization
	\item Resilience to random failure due to graph non-uniformity
	\end{itemize}
\item Expose priority based multi-class data handling
	\begin{itemize}
	\item Assured delivery: archival data
	\item Expedited delivery: ``interesting'' data, events,
	or control messages
	\end{itemize}
\item Automated inference of class of data based on in-network
data processing
\item Can be designed to aid spatial correlation of data
\end{itemize}
\end{slide}

\begin{slide}{Routing}
\begin{itemize}
\item {\RED Challenges:}
	\begin{itemize}
	\item Communication reliability
	\item High energy cost of tx \& rx
	\item Need to rely on local information due to
	bandwidth and buffer limitations
	\end{itemize}
\end{itemize}
\end{slide}

\begin{slide}{Current Status}
\begin{itemize}
\item 
All of the sensors have been calibrated, a network of these sensors
has been built and data can be collected from these sensors.
(Note that all of the aforementioned work addresses practical issues
of scalability, power savings, and noise reduction).
\item
Primary primitives of multicast and convergecast have been implemented.
\item
TinyFFT has been developed and benchmarked on a linux PC. The performance
characteristics are encouraging -- i.e., it appears likely that we will
be able to perform 128 point integer-based FFTs on the motes.
\item
TinyFFT is being ported to the Atmega microcontroller.
\end{itemize}
\end{slide}

\begin{slide}{Immediate Goals}
\begin{itemize}
\item
Complete TinyFFT and TinyKalman libraries.
\item
Complete in-network spatial aggregation.
\item
Validate the entire communications infrastructure and deploy the network.
\end{itemize}

Our target for getting this done is end of summer.
\end{slide}

\end{document}
