Structural Health Monitoring Using Smart Sensors - ideals ...

Chapter 5
MIDDLEWARE SERVICES
In this chapter, middleware services for smart sensors are studied and realized.
Among the middleware services are data aggregation, reliable communication, and
synchronized sensing (Nagayama et al., 2007). These middleware services are
implemented on the Imote2 running TinyOS, while ideas behind these services are
generally applicable to other smart sensor platforms.
5.1 Data aggregation
The amount of data transferred in SHM applications is considerable. Long vibration
records are acquired from densely distributed smart sensors. If they are collected at a
single sink node using multihop communication, communication time easily exceeds the
time necessary for any other smart sensor task. The amount of data utilized in SHM
applications is first estimated. Distributed estimation of correlation functions is then
proposed as model-based data aggregation (Nagayama et al., 2006b, 2007; Spencer &
Nagayama, 2006). This data aggregation method is scalable to networks of large numbers
of smart sensors. Implementation issues are then discussed.
5.1.1 Estimate on data amount in SHM applications
To capture the vibration response of civil infrastructure, sensors need to acquire data
at an appropriate sampling rate for a sufficient period of time at various locations.
Analysis methods are among the factors in determining these parameters. While there are
many analysis methods for SHM applications, most output-only-measurement approaches
for civil infrastructure utilize Power Spectral Density (PSD) or Cross Spectral Density
(CSD) estimation. The amount of data involved in spectral density estimation for civil
infrastructure, thus, can be estimated.
The number of data points used in spectral density estimation is determined by the
number of FFT data points and the number of averages. Spectral density estimation is
performed by averaging the outcomes of FFT analyses. Though any power of two
theoretically works as the number of FFT data points, 1,024 or 2,048 points are most often
used and give reasonable results. The error in the spectral density estimation is inversely
proportional to the number of averages. A larger number of averages is, therefore,
advantageous. The drawback of having a large number of averages is the associated large
amount of data and long measurement time. If the measurement time is too long,
environmental conditions such as wind velocity and temperature may change during
measurement; averaging signals measured when a structure is behaving in a transient
manner is not desirable. The number of averages in spectral density estimation practically
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