Structural Health Monitoring Using Smart Sensors - ideals ...

3. Fault tolerant system
Message packet loss tolerance
RF communication inherently has data loss. Resending lost data packets can increase
transmission reliability at the price of increased communication demand and power
consumption. Algorithms tolerant of data loss are appealing from both power and
bandwidth perspectives.
Node failure tolerance
During the life of a smart sensor node, functionalities of the node may become
impaired. Power depletion stops all of the functionalities of a smart sensor. Wireless link
disconnection impairs only the communication capability. The network should be tolerant
of these node losses by reconfiguration of networks, while application algorithms
allowing the loss of sensing signals from a few nodes are desirable.
Byzantine error tolerance
Even when data is acquired, the data can be faulty and misleading. For example,
when a node with an accelerometer comes unglued, the node acquires faulty acceleration
data from the sensor. As another example, RF interference could induce errors in received
data. Low battery power may also result in random errors in the radio, CPU, and/or sensor.
A system to address this Byzantine error problem is desirable.
4. Desirable algorithmic characteristics specific to SHM for civil infrastructure
Multiscale information
SHM techniques are needed that can employ measured information at multiple scales.
Different types of sensors measure different physical quantities, each of which has its own
sensitivity to certain structural conditions. For example, acceleration and strain are among
the most important physical quantities to judge the health of a structure. While
acceleration measurements are essentially global responses of a structure, structural strain
provides an important indicator of local structural behavior. By using such multiscale
sensor information, structural condition is expected to be assessed more accurately.
Collaboration in local sensor communities
Smart sensors, densely distributed over structures, are expected to communicate with
each other, at least in a local sensor community, to make efficient and effective use of
measured data. Closely located nodes are anticipated to give highly correlated
measurements, which might be fused without significant loss of information, although the
definition of closeness depends on each measurand. Data processing of a spatially
sampled measurand may reveal important information, in a similar way to data processing
of a measurand sampled in the time domain. For example, estimation of a mode shape and
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