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USC Computer Science Technical Reports, no. 866 (2005)

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USC Computer Science Technical Reports, no. 866 (2005)

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Content Structural Damage Detection and Localization
Using NETSHM
Krishna Chintalapudi
∗ , Jeongyeup Paek
∗ , Om Prakash
∗ , Tat Fu
†
, Karthik Dantu
∗ ,
John Caffrey
†
, Ramesh Govindan
∗ and Erik Johnson
†
∗ Computer Science Department,
University of Southern California,
Los Angeles, CA 90089, USA
†
Civil Engineering Department,
University of Southern California,
Los Angeles, CA 90089, USA
Abstract— Structural health monitoring (SHM) is an important
application area for wireless sensor networks. Recent work has
examined the design of wireless sensor networks for structural
data acquisition systems. The eventual goal of structural mon-
itoring, however, is autonomous detection and localization of
structural damage. Developing practical damage detection and
localization schemes is still an area of active research and there
exists a large array of techniques. Structural engineers typically
implement and test SHM algorithms in a higher level language
such as C/Matlab. Practical sensor network based SHM systems,
thus, should allow structural engineers to program various SHM
schemes in a higher level language that they are familiar with
such as C/Matlab without delving into the intricacies of the
underlying sensor network. In this paper we describe the design
of NETSHM, a programmable, re-usable and evolvable sensor
network system that can be used to implement a variety of
structural monitoring techniques at a level of abstraction that
structural engineers are comfortable with. We validate our design
by implementing and evaluating a damage detection techniques
and localization techniques on a NETSHM prototype.
I. INTRODUCTION
Structural health monitoring (SHM) [1] is a vast, inter-
disciplinary area of research whose literature spans several
decades. It has been suggested that networked embedded sens-
ing technology has a role to play in structural monitoring [2],
[3]. Recent work has demonstrated the feasibility of contin-
uous structural data collection using a wireless network [4],
[5], [6]. This paper takes the next step in the evolution of this
research, examining how wireless sensor networks might be
employed for more sophisticated structural monitoring tasks.
The central focus of SHM research is the detection and
localization of damage in a variety of structures. Broadly
speaking, SHM techniques for detecting and localizing damage
(henceforth, SHM techniques) rely on measuring structural
response to ambient vibrations or forced excitation. Ambient
vibrations can be caused by earthquakes, wind, or passing
vehicles, and forced vibrations can be delivered by hydraulic
or piezoelectric shakers. A variety of sensors, such as ac-
celerometers, strain gauges, and displacement sensors can be
used to measure structural response. SHM techniques infer the
existence and location of damage by detecting differences in
local or global structural response before and after damage.
Our goal in this paper, is to architect a programmable,
re-usable and evolvable software system for implementing
SHM techniques on wireless sensor-actuator networks that
SHM engineers can use without having to delve into the
vagaries of the underlying sensor network. To achieve this goal
we start by exploring the various existing SHM techniques
and identifying their requirements and constraints. We then
consider the question, “are these schemes amenable to in-
network processing and duty-cycling” - two principles that are
crucial for deploying long-lived sensor networks. A significant
challange in the use of wireless sensor networks for SHM are
high data rate requirements - up to 5 Mbps for a hundred
sensors (Section II-D). So, it would seem difficult to design a
long-lived network of wireless sensors that can sustain these
data rates. In Section III we show, two simple ways in which
most centralized SHM techniques can be realized on a long-
lived network of wireless sensors. We rely on the observation
that a structure’s response need not be continuously monitored.
Thus, the monitoring can be triggered by large ambient
vibrations, or by scheduled tests of the structure. Such tests
are usually conducted by exciting a structure using shakers.
In between these triggers, the entire network can enter sleep
mode to extend network lifetime. Furthermore, some of the
SHM techniques are amenable to local processing, where
raw sensor data is processed before being transmitted to
the base station, further conserving energy. We show how a
previously proposed localization technique can be adapted to
take advantage of local processing.
We describe the NETSHM architecture which permits struc-
tural engineers to program various SHM applications without
having to understand the intricacies of wireless networking in
Section III. Large parts of the systems software in NETSHM
can be re-used across applications, and the architecture is
designed so that applications can transparently take advantage
of increasing sensor node capabilities as technology evolves.
The NETSHM architecture (Section III) is predicated on
the observation that, to deal with the data rate requirements
of SHM applications, wireless sensor-actuator networks for
structural monitoring will be hierarchical. These networks
will consists of two tiers: the lower-tier comprised of mote-
class wireless sensor nodes enable flexible deployment on
a structure, and an upper-tier comprised of higher-capacity
nodes (either PCs or Stargate-class nodes) that provide the
bandwidth scaling.
NETSHM leverages this hierarchical network to realize
a novel functional decomposition between the two tiers that
meets the requirements discussed above. In NETSHM, all ap-
plications run on upper-tier nodes. Applications task individual
motes to collect, and possibly process, data. Motes transmit the
raw or processed data to the relevant upper-tier node, perhaps
via multiple hops. Such an architecture permits re-use of the
lower-tier systems components, and a flexibly designed tasking
interface can let applications use increased processing power
on the motes as the technology evolves.
Our NETSHM prototype (Section IV) is a fairly complete
realization of this architecture. Applications in NETSHM are
written using Matlab or C, languages familiar to structural
engineers. We have implemented a routing subsystem that
enables communication between the two tiers, and a reliable
delivery mechanism for conveying tasks and results between
the two layers. We have also implemented a tasking interface
that permits triggered data collection from the lower-tier.
Using this prototype, we have implemented two qualitatively
different SHM applications, one which detects damage using
shifts in modal frequencies, and another which localizes dam-
age based on estimating changes to mode shapes.
We have extensively (Section V) evaluated these applica-
tions on a scaled model of a 4-story building. We find that
our applications are able to detect and localize 100% of the
damage patterns we studied with very low false positive rates
and zero false negatives. We also find that these applications
are also amenable to a highly energy-efficient implementation.
We have also evaluated a larger deployment of a NETSHM
prototype on a full scale imitation hospital ceiling. We find
that NETSHM can provide robust, low-latency triggered data
acquisition even in realistic wireless environments.
II. SHM TECHNIQUES AND WIRELESS SENSOR
NETWORKS
In this section, we describe the physics underlying struc-
tural response and survey SHM techniques. Our exposition is
aimed at a general audience, and is designed to motivate the
NETSHM system architecture, which we present in the next
section.
In structures, a damage to one or more members (columns,
cables, braces etc.) manifests itself in the response of a
structure to external forces. SHM techniques leverage this
property by using a history of structural response in order
to determine whether (detection) and where (localization)
damage has occurred. One class of SHM techniques attempts
to detect minute, incipient damage by detecting changes in
local material properties (e.g., tiny cracks or corrosion) in
various areas of the structure. Typically, such local SHM
techniques use sophisticated systems (high-frequency ultra-
sound, thermal or x-ray imaging) that are well beyond the
reach of miniaturization. We do not consider these techniques
in this paper.
Our focus is on global SHM techniques which detect and
localize damages that significantly alter the characteristics of
the entire structure: e.g., a damage to a bridge cable, or a brace
in a building. The responses of large structures usually com-
prise frequencies in the tens of Hz, and can be sensed using
relatively inexpensive low-noise MEMS-based accelerometers.
Structures can be excited by ambient vibrations caused, for
example, by wind or passing heavy vehicles. Increasingly,
the structural engineering community is becoming interested
in active sensing [2] techniques which measure structural
response to forced excitations. These excitations are usually
delivered by hydraulic or electro-magnetic shakers attached
to the structure. A wide variety of shakers are commercially
available today, and the choice of the shaker is determined
by the structure’s properties. Forced excitations promise more
accurate damage detection and localization, since (unlike with
ambient excitations) the input to the system is known and
controlled.
A. Basics of a Structural Response
When subjected to external forces, the response of a struc-
ture is conceptually similar to the response of a vibrating string
or a metal plate. Upon excitation, a structure vibrates as a
combination of several harmonics known as modes. Each mode
deforms the structure into a particular spatio-temporal pattern
known as the mode’s shape. The mode shapes and frequencies
of a structure depend on the material properties, structural
geometry and assembly of its constituent members. A damage
in some of members of a structure results in a change in mode
shapes and frequencies induced in the structure.
The dynamics of a structure are often succinctly expressed
by the linear differential equation
1
,
M¨ y +C˙ y+Ky =F(t). (1)
Here,M,C andK are matrices containing the masses, damp-
ing factors and the stiffness coefficients of the various elements
of the structure andF is a vector specifying the external forces.
y =

y
1
··· y
n

T
is a vector containing the positions of
various locations in the structure. When F is an impulse, the
response of such a system y(t)) can be expressed as,
y(t) =
l=m
X
l=1
a
l
v
l
e
− η l
t
cos(ω
l
t+φ l
). (2)
In Equation 2 ω
l
,η l
, a
l
and φ l
represent the natural frequency,
the attenuation factor, and the amplitude and phase of the
l
th
mode/harmonic induced in the structure respectively. The
normalized modal vectors, v
l
, characterize the mode shape,
the spatial shape of the harmonic induced in the structure at
that resonant frequency scaled such that kv
l
k = 1.
1
This equation assumes that all perturbations are small enough so that
material properties may be considered linear.
B. A Brief Survey of SHM Techniques
Fundamentally almost all SHM techniques rely on changes
in mode shapes or frequencies relative to those in the un-
damaged structure. The SHM literature is vast, so we do not
attempt to be exhaustive in our coverage. Rather, we classify
these techniques localization techniques by approach, and then
provide examples of each approach.
Damage Detection Techniques: Broadly speaking, the
damage detection literature falls into four categories. Sohn et
al. [7] propose a time series based scheme which models struc-
tural response using auto-regressive (AR) or auto-regressive
moving average (ARMA) linear time series prediction models.
Damage is identified by a “significant” shift in the AR/ARMA
coefficients from the coefficients of the undamaged system.
Crawly et al. [8] detect and locate damage in composite
materials using the ratio of mode frequency shifts between
two modes. Srinivasan et al. [9] find that changes in mode
shapes can be more sensitive to damage than changes in modal
frequencies. Finally, several pieces of work have examined
non-linear neural network based approaches for SHM (e.g.,
Elkordy et al. [10]). The reader is referred to Doebling et
al. [11] for a very comprehensive survey of damage detection.
Damage Localization: Damage localization algorithms
use changes in structural response to deduce changes in
physical properties (e.g., stiffness) of the members in the
structure. There exist two classes of approaches to damage
localization: time-domain and frequency-domain methods. Al-
most all existing time-domain localization techniques e.g., [12]
attempt to solve the inverse problem of estimating all or some
of the system parameters. A reduction in stiffness (inferred
from the estimated stiffness matrix) in a member/region in-
dicates damage to that member/region. More recent damage
localization schemes operate in the frequency domain and rely
on estimating changes in stiffnesses using changes in mode
shapes computed from the responses obtained from several lo-
cations in the structure e.g., [13]. An essential feature common
to both time and frequency domain schemes is that existing
localization techniques are fundamentally centralized i.e., are
not amenable to in-network aggregation or colloboration.
Finally, the number of sensing points is an important
consideration in the practical deployment of damage local-
ization schemes. A structure may typically have hundreds of
members and will require at least two tri-axial sensors sensing
accelerations at each end of every member of the structure
to detect damaged members. Another approach is to divide
the structure into sections and instrument tri-axial sensing at
every corner of each section to determine the damaged sections
of the structure. The resolution and efficiency of a damage
localization schemes depends on the density of placement
of the sensors. A practical damage localization scheme may
require a highly dense deployment involving a large number
of sensors.
C. SHM and Wireless Sensor Networks
Can the SHM techniques discussed above benefit from
wireless sensor network technology? If so, what constraints
does the SHM application place on sensor networks? In this
section, we discuss these questions.
In Section II-B, we argued that mode shape estimation may
require several hundred sensors deployed on a large structure.
Delivering power and wired networking to these sensors is a
challenge. The location of power outlets in large structures is
often uncorrelated with the locations of structural members,
and the cost of adding cabling to these structures (existing or
newly built) is prohibitively high [15].
Wireless sensor networks are perfectly suited for structural
monitoring in general, since they offer a great deal of flexibil-
ity in sensor placement. Part of this flexibility comes from hav-
ing un-tethered nodes. An equally important part comes from
the multi-hop capability of wireless sensor networks: large
structures are harsh wireless environments where connectivity
can vary with time, and engineering a sensor placement such
that each sensor node is within one hop of a base station is
difficult, if not impossible.
On the other hand, wireless sensor networks appear to
be unsuitable for implementing global SHM techniques. As
Section II-B shows, most damage detection and localization
techniques estimate the characteristics of the structure as a
whole (modal frequencies, or mode shapes) and are therefore
fundamentally centralized. Put differently, unlike some other
sensor network applications such as target localization [16]
these techniques are not amenable to implementation using
collaborative signal processing among a subset of sensors.
It would seem, then, that SHM techniques are not amenable
to energy-efficient realizations in long-lived wireless sensor
networks. We estimate that in practice, a network lifetime of
several months is necessary for this application. This timescale
corresponds to the time between building inspections, at which
time failed nodes can be replaced or repaired.
Despite this, we argue that SHM techniques can be im-
plemented in an energy-efficient manner in wireless sensor
networks. Our claim is based on the observation that the
structural response need not be monitored continuously; the
duty cycle of collecting structural response can be adjusted
to achieve the desired network lifetime. We see two distinct
energy-efficient implementation models for SHM techniques.
Triggered: In a triggered implementation, sensors are trig-
gered by a periodic timer, a significant ambient vibration, or
operator action, to collect structural response and transmit the
raw sensor data (e.g., vibration time series obtained from an
accelerometer) to a central node. We now sketch two instances
of this model, one for SHM techniques that rely on forced
excitation, and another for techniques that rely on ambients.
For example, consider a structure equipped with one or more
shakers. On such a structure, sensors and shakers might be
programmed to synchronously wake up once day and test the
structure. The shakers can be tasked to test the structure using
a pre-defined excitation method. Multiple shakers might be
used in a single test. A typical test can last anywhere from a
few seconds to a few minutes. Sensors collect the structural
response to the test, then transmit the data back to a central
node. After having collected the data, the sensors go into
deep-sleep mode. As another example, consider a wireless
sensor network that is deployed to measure structural response
to ambient vibration. All sensor nodes operate in deep-sleep
mode, but have an ultra low-power specialized co-processor
that, using an attached accelerometer, can detect the onset of
significant ambient vibration (other ways of achieving this are
possible, of course; note also that this is a hypothetical design
and we know of no working system that has been designed
thus). The co-processor can wake up the sensor, which collects
the vibration data, then transmits the raw sensor data to the
base-station, and puts itself in deep-sleep mode.
Triggered with Local Computation: This second imple-
mentation model permits more aggressive energy savings.
It is identical to the triggered model, with one exception:
sensor nodes process the raw sensor data prior to sending
it to a central node. This corresponds to a decentralized
implementation of an SHM technique. For example, consider
an SHM technique that uses shifts in modal frequency to detect
structural damage. This technique can be implemented using
this implementation model, since each sensor can perform an
FFT on the raw sensor data, and transmit only the modal
frequencies to the central node (idenified as peaks in the spec-
trum). We discuss this algorithm in greater detail in Section V-
B. In that section, we also describe a decentralized version of
a previously published damage localization algorithm.
We conjecture, though we cannot prove, that any global SHM
technique can be implemented using the “Triggered” model.
We also believe that many, but not all, SHM techniques
can also be implemented using the “Triggered with Local
Computation” model. Given the fundamentally centralized
nature of global SHM techniques, the converse might also
be true: that all energy-efficient implementations of global
SHM techniques conform to one of these two implementation
models. Given this, we ask the question:
How can we architect a re-usable and evolvable
software system that enables us to program most
SHM techniques using one of the two models de-
scribed above?
The next section discusses the architecture of the NETSHM
system, and the subsequent sections describe the design and
implementation of that system, as well as our evaluations of
NETSHM.
D. SHM Requirements
Before we do that, however, we discuss the requirements
placed on NETSHM by the SHM application domain.
Reliable Transmission: SHM techniques require reliable
transmission of data to the central node. Damage manifests
itself as subtle changes to the global characteristics of the
structure, and the loss of a few samples can critically impact
the accuracy of an SHM technique.
Time Synchronization: Global time synchronization among
sensor nodes e.g., [18] is crucial to SHM. Time synchro-
nization errors are manifested as errors in the estimated
phases (φ l
). The error introduced in a mode shape due to a
synchronization error of τ seconds depends on sin(τω ), where
ω
2π is the frequency of the mode. As a practical example, a
mode of 20Hz requires time synchronization accurate to within
80 μ s to guarantee less than 1%.
High Data Rate: Most structures have natural frequencies
of a few Hz or a few tens of Hz. Structural engineers
recommend sampling at about 10 times the highest dominant
modal frequency to overcome the effects of noise and high
damping in the structure. Thus, sampling rates in the 200-
500 Hz range should suffice for global SHM methods. A tri-
axial accelerometer generating 16-bit samples at 500 Hz (most
SHM applications demand highly sensitive sensors with low
noise characteristics) generates a data rate of about 50Kbps
including headers. As described in Section II-B dense sensing
deployments are crucial to damage localization algorithms. A
bridge with 100 such sensors can generate a 5 Mbps aggregate
burst of traffic. We note that this data rate is an order of
magnitude higher than that required for many other sensor
network applications and also exceeds the ability of existing
sensor network platforms such as mica-Z and telOS.
III. NETSHM SYSTEM ARCHITECTURE
In this section, we discuss the architecture of NETSHM,
a software system for implementing many of the SHM tech-
niques described in Section II-C.
A. Goals
In addition to satisfying the requirements discussed in
Section II-D, we set three design goals for NETSHM. In
what follows, we refer to an SHM technique implemented on
NETSHM as an SHM application.
First, NETSHM should present a programming abstraction
familiar to structural engineers. In particular, the NETSHM
programmer should not be exposed to the intricacies of wire-
less communication, energy management, and device resource
constraints. Without this requirement, we see little likelihood
of sensor networks being adopted in this application domain.
Second, NETSHM must be designed to be re-usable in two
distinct senses of the term. System components should not
have to be re-designed or re-implemented for different SHM
applications. For instance, one should not have to write a new
routing or time synchronization protocol when implementing
a new SHM application on NETSHM. This is a very difficult
objective since the space of SHM techniques is large. In addi-
tion, we require that a deployed instance of NETSHM should
allow multiple SHM applications to execute concurrently. This
level of re-use enables users, for example, to improve the
accuracy of damage localization by concurrently running two
qualitatively different damage localization algorithms.
Finally, NETSHM applications should not have to be re-
implemented as technology evolves. The mote-class devices
today cannot support some of the signal processing tasks that
SHM applications demand; given their memory constraints, it
is possible to implement Fast Fourier Transforms on the motes,
but just barely so. However, as these devices evolve to have
more on-board processing and memory, the NETSHM system
should be able to transparently make use of these resources in
order to increase system lifetime.
B. A Strawman Architecture
How should we architect NETSHM to satisfy these goals?
One possible approach is as a flat network of mote-class
devices which contain embedded application-specific func-
tionality. This is the architecture envisioned in early sensor
network work [19] and implicitly followed in the subsequent
literature [20], [21], [22]. In such an architecture, an SHM
application would be compiled into application-specific code
that could then be distributed to individual nodes using some
code distribution mechanism [23], [24]. Such an architecture
permits significant collaborative in-network aggregation and
can result in increased network lifetimes.
We argue that such an architecture is ill-suited for global
SHM. since global SHM techniques are fundamentally cen-
tralized and detect damage by measuring changes to global
structural characteristics. For this reason, we do not believe
that an architecture that permits in-network collaborative pro-
cessing can reduce communication costs significantly. Rather,
it will suffice for the NETSHM architecture to support local
processing of raw sensor data. This can be used to implement
SHM applications using the “Triggered with Local Computa-
tion” model (Section II-C).
However, we believe that not all SHM applications can
be decentralized to allow for local computation. For these,
the capacity constraints of a flat network of motes will
limit scaling significantly. To meet the data rate requirements
(Section II-D) of such applications, the NETSHM architecture
will have to incorporate hierarchical elements. Specifically,
we believe that any large-scale SHM system will, for the
foreseeable future, be two-tiered. The lower tier will con-
sist of mote-class wireless sensor devices that enable easily
deployable dense instrumentation. These motes will contain
sensors (such as accelerometers or strain gauges), and may
be attached to shakers. The upper tier will contain a small
number (perhaps an order of magnitude fewer than the number
of wireless nodes) of nodes (such as Stargates, or even PCs, or
a combination thereof), which can be powered and which have
significantly more processing, memory, and communication
resources. The upper-tier provides the bandwidth scaling for
SHM applications.
As an aside, one might argue that if structures are tested
infrequently, or if significant ambients occur rarely, one can
trade-off latency for lower bandwidth (i.e., transmit the raw
sensor data over time). However, we believe NETSHM should
support reasonable latencies, especially after the occurrence
of a catastrophic event when it might be necessary to quickly
conduct repeated tests to assess the extent of damage in a
structure.
C. The NETSHM Architecture
NETSHM employs a different, yet simple and intuitive
architecture for two-tiered sensor/actuator networks (Figure 1).
In NETSHM, SHM applications run on an upper-tier node
(e.g., a PC). An application may run on any node. While
our current prototype does not permit this, the NETSHM
architecture allows multiple applications to run concurrently.
Restricting programs to run on upper-tier nodes is motivated
by the signal processing needs of SHM applications. As tech-
nology advances, upper-tier nodes will always have far more
processing and memory than lower-tier nodes (motes) and
SHM applications can leverage these resources to improve the
quality of detection and localization. In our current prototype,
users write programs in Matlab or C (the two most popular
environments in the SHM community).
The motes in the lower-tier are individually addressable
by upper-tier nodes. This means that an SHM application
can address a message to a specific mote. The architecture
itself does not specify what form this addressing takes (e.g.,
geographic, flat, or hierarchical). This individual mote address-
ability contradicts what previous research has assumed [20],
but more naturally captures the way structural engineers
think about SHM techniques. Specifically, their descriptions
of algorithms employ spatial concepts (like mode shapes), and
being able to specify individual sensors or sensing points is
a useful capability that helps them program SHM techniques.
individual mote addressability enables SHM applications to(i)
to selectively address individual shakers, and (ii) to address a
subset of motes in an over-engineered deployement.
SHM applications can send messages to individual motes,
but they cannot perform arbitrary computations on them. In the
NETSHM architecture, an application is restricted to tasking
a mote in one of four ways:
• Collect raw sensor data within a specified time interval
and with specified sensor parameters (e.g., sampling rate),
and transmit it back to the upper-tier node running the
application.
• Collect raw sensor data within a specified time interval
and with specified sensor parameters (e.g., sampling
rate), but locally process the raw samples and transmit
the processed data to the upper-tier node running the
application. At any given instant, a mote exports a pre-
defined library of processing functions. This library can
evolve over time. This library can contain functions rang-
ing from simple windowed averaging and thresholding,
to more complicated computation of FFTs and ARMA
coefficients.
• Actuate an attached shaker using a specified type of
excitation (Section II-C) at a specified time, or according
to a specified schedule.
• Get and set device parameters and statistics. This can be
used for monitoring and management of the lower-tier
devices.
This constrained architecture satisfies the goals described
in Section III-A. It enables us to implement the “Triggered”
and “Triggered with Local Computation” models. Consider
an SHM application based on forced excitation. This appli-
cation, if implemented using the “Triggered” model would
periodically task all (or a subset of) motes to collect data for
a small time interval, and would task one or more shakers
to excite the structure using an application-specific pattern
or schedule. The application would then process the received
data, compare it with historical measures, and raise an alert
if necessary. An application implemented using a “Triggered
with Local Computation” model would be implemented very
similarly, but would additionally task the motes to process
the data (e.g., compute the ARMA coefficients of the raw
sensor data) in the desired fashion. If the motes were incapable
of performing this computation, the upper-tier node could
do so transparently to the application. With the evolution of
technology, this computation could be pushed out to the motes
without modifying application code (cf. the evolvability goal in
Section III-A). Finally, SHM applications relying on ambients
would be implemented similarly with two exceptions: they
would (obviously) not need to task shakers, but would task
sensors to collect and transmit data exceeding some threshold.
This constrained architecture has the attractive property that
it clearly defines and standardizes the functionality in the
lower (mote) tier. This enables re-use of mote tier functionality
across applications, a goal of NETSHM design. This comes at
the cost of some efficiency: it is at least theoretically possible
that some collaborative processing can reduce the amount of
communicated data. However, we believe this drawback is
not serious in practice (a) because we suspect that the data
reduction beyond local processing is likely to be small, and
(b) that in a large structure NETSHM deployments can be
architected using a small number of hops (3-4) so the cost of
this approach is relatively small. The NETSHM architecture
does not come at the expense of robustness, as we discuss in
the next section.
In summary, NETSHM leverages hierarchy to design a
constrained architecture where applications execute on upper-
tier nodes, and only generic computation is permitted on
the lower-tier nodes. Others have suggested the need for
hierarchical sensor networks [25] in general and for structural
monitoring in particular [26] but, to our knowledge, no one
else has asserted such a separation of functionality between
layers of a multi-hop wireless hierarchical network for the
purpose of designing a re-usable and evolvable system for
programming SHM applications.
IV. NETSHM DESIGN AND IMPLEMENTATION
In this section we describe the design and implementation of
a NETSHM prototype that adheres to the system architecture
described above.
A. The Programming Interface
One explicit goal of NETSHM is to raise the level of
abstraction for programming SHM applications to the point
where structural engineers should not have to understand the
intricacies of sensor networking. Without this, we believe it is
unlikely that sensor networks will be used for SHM. To guide
our choice of the NETSHM programming interface, we look to
current practice in the structural engineering community. Many
structural engineers extensively simulate models of structures
in software before prototyping or building them. Matlab [27]
is often used for analytical models, and NASTRAN [28]
(implemented in C) for finite-element modeling.
Our NETSHM prototype provides programmers with a suite
of Matlab and C functions that they can use to implement
a variety of SHM applications. Our programming interface
closely follows the tasking interface described in Section III-C.
A triggered SHM application implemented using this interface
first creates one or more logical groups of motes that need
to participate in the application. This group construct is a
convenient abstraction for addressing collections of lower-tier
nodes. The application then tasks a group to start collecting
vibration data at a specified relative time with a specified
sampling rate. It also tasks a group of shakers to excite
the structure at a specified time. The results of this test are
returned asynchronously to the SHM application, and can be
manipulated by the application as a Matlab dataset. Figure 6
shows an example of a damage detection application written
using this interface
2
. We describe this example in greater detail
in Section V-B.
B. The Software Structure
NETSHM consists of two distinct stacks, one for the upper-
tier nodes (Figure 2), and one for the motes (Figure 3).
At the top of the upper-tier stack, the NETSHM program-
ming interface is built on top of a task library. In our current
prototype, the task library is implemented in C, and the
NETSHM Matlab interface functions make calls to the library
via Matlab Mex function wrappers. The library translates the
interface functions into a sequence of tasks that are transmitted
to motes. It invokes a reliability mechanism implemented on
top of a robust routing layer. These two components are
described below.
The task library itself is conceptually simple. Interface calls
to create node groups create local state in the library that
associate a group identifier with a set of nodes. In principle, the
library would interface with a resource discovery component
for determining the identity, location, and other characteristics
of the motes deployed on a structure. In our current implemen-
tation, this information is manually configured. We believe this
to be a reasonable short-term compromise: (i) automated node
discovery, identification and localization are hard research
problems, and (ii) placing sensors on structures typically
involves a great deal of planning and human intervention.
Interface calls to command a group of nodes to collect sensor
data, or to excite the structure are translated by the task
library into messages that are individually sent to the relevant
2
In the code snippets, functions beginning with “NetSHM” are part of the
interface. We have omitted a detailed description of the NETSHM API for
brevity.
Fig. 1. The organizational hierarchy of Net-
SHM
Fig. 2. NetSHM Stack on the gateway nodes Fig. 3. NetSHM stack on the mote class nodes
motes. Our current stack does not export a multicast delivery
abstraction, and we have left this to future work.
The mote stack is similarly implemented. Tasks from SHM
applications are delivered using the routing layer to the ad-
dressed mote. At each intermediate hop, network packets may
be retransmitted to improve the likelihood of delivery. At the
destination, the reliability layer implements end-to-end error
recovery and sequenced delivery to the tasking layer. This
latter layer interprets the tasking commands and executes them
appropriately. Executing a task may involve activating a sensor
or a shaker at a specified time, and then processing raw sensor
data. Results from a task are delivered reliably back to the
corresponding upper-tier node. In our current prototype, we
have not implemented any local processing, since even the
most basic SHM processing (an FFT on vibration samples) is
beyond the reach of the platform NETSHM currently executes
on (the Mica-Z). In our experience, the memory constraint
on this platforms inhibits this functionality, and we believe
that such local processing can be implemented on the next
generation of sensor platforms such as the Tmote Sky [29] or
the Intel Research motes.
The current NETSHM prototype lacks two important com-
ponents: support for the execution of multiple concurrent SHM
applications, and support for duty cycling. These components
require substantial research and are beyond the scope of this
work. Support for concurrent execution can be implemented
by a distributed resource management layer at the upper-tier,
which knows about and arbitrates between overlapping tasks
issued to one or more motes. Support for network-wide duty-
cycling can leverage prior work on the design of long-lived
sensor networks [30], but needs to support unpredictable task
arrivals.
C. Routing
A core component of the NETSHM system is the module
that supports robust dynamic routing. Unlike traditional com-
munication networks, NETSHM does not require any-to-any
routing. Rather, upper-tier nodes need to be able to commu-
nicate with any mote, and vice-versa. A mote in NETSHM
never originates a message destined to another mote. Upper-
tier nodes can communicate with each other; the NETSHM
architecture does not disallow a distributed implementation of
an SHM application on the upper-tier motes. The requirements
for robustness in NETSHM are, however, the same as in
other networks: as long as there exists a communication path
between an upper-tier node and a mote, the two of them should
be able to exchange packets.
Before we discuss NETSHM routing, we describe node
addressing in NETSHM. All NETSHM nodes (upper or lower
tier) derive their addresses from the same flat address space.
In our implementation, lower tier nodes use the TinyOs node
IDs while the upper tier nodes are manually configured. Ad-
mittedly, flat addressing does not scale beyond a few hundred
nodes and devising scalable, auto-configurable addressing is a
key challenge for NETSHM. Also left to future work is group
addressing of the motes.
We can split the routing system design into three different
parts: any-to-any routing within the upper-tier node, routing
from gateways on the upper tier to motes, and routing from
motes to an upper-tier gateway. Gateways are distinguished by
having at least two network interfaces, at least one of which is
for the mote network, and another for the upper-tier network.
Any-to-any routing on the upper-tier can potentially bor-
row solutions from the ad-hoc routing literature [31]. For
expediency, we chose to implement a simple distance-vector
type scheme on the upper-tier. This routing protocol not only
disseminates routes to upper-tier nodes, but also distributes
routes to the motes imported from the gateways (described
below). Routes to motes are tagged with the address of the
gateway, enabling upper-tier nodes to reach any mote. This
design is conceptually similar to the kind of hierarchical
routing employed in the Internet.
For routing from motes to an upper-tier gateway, our
prototype uses code from the CENS Extensible Sensing Sys-
tem [32]. This system constructs several routing trees in
the lower tier, one tree rooted at each gateway. To achieve
this, each gateway periodically floods a beacon on the mote
network. Any mote that receives this beacon from a neighbor
picks that neighbor as its parent on the tree rooted at the
corresponding gateway. If multiple gateway beacons are heard
at a mote, that mote chooses the “best” gateway. The best
gateway is that which incurs the lowest cost path, where the
cost metric is similar to ETX or MT [33], [34].
For routing from an upper-tier gateway to the motes, we
have implemented a simple flat routing protocol built on this
forest of trees. In our protocol, each node propagates to its
parent reachability to all nodes within the subtree rooted
at itself. This is achieved using a periodic routing table
transmission to the parent. Eventually, the gateway receives
routes to all motes within its subtree, and it then exports
these routes into the upper-tier any-to-any routing protocol as
described above.
D. Reliable Delivery
NETSHM provides reliable delivery of tasks from upper-tier
nodes to motes, and raw samples or processed data from motes
back to the corresponding upper-tier node. In NETSHM, reli-
able delivery is transactional in the sense that data is always
sent in response to a task. However, these transactions can be
asynchronous. The response to a task can be received well
after that task is issued by the SHM application. For example,
a task might require a sensor to respond at a certain time,
or in response to a significant external event. Furthermore,
these transactions can be asymmetric, since task descriptions
are concise, but responses to tasks can transfer significantly
more data. These two differences motivate different reliable
transfer abstractions and provide opportunities for optimized
implementations relative to existing reliable delivery mecha-
nisms such as TCP.
Rather than provide a single reliable delivery abstraction
between the two tiers, NETSHM leverages the asymmetry in
the underlying architecture to provide two different reliable
delivery abstractions: reliable packet delivery from the upper-
tier nodes to motes (for tasks), and reliable and sequenced
stream delivery from the motes to the upper-tier (for data).
We now discuss the implementation of these abstractions.
Because tasks are smaller and fit in one packet, the imple-
mentation of the packet delivery abstraction can be optimized
in two ways. First, packet delivery avoids the overhead of
connection establishment and teardown, relying instead on a
simple end-to-end acknowledgment. Second, this more spe-
cialized abstraction avoids the need to provision large buffers
for sequencing data on the resource-constrained motes.
The stream delivery abstraction is conceptually very similar
to TCP, but the implementation details are subtly different.
Before we discuss this, however, we point out an important
requirement imposed by the asynchronous nature of reliable
communication in NETSHM. Applications need to be able to
uniquely associate a task with responses that might arrive well
after the task is issued. One way to do this would be to include
the complete task description in every response. The reliable
delivery layer can use this to de-multiplex responses to the
appropriate applications, and the applications can use this to
associate responses to tasks. However, this can add significant
overhead to every message, resulting in poor throughput.
For this reason, NETSHM allows applications to request
3
3
Not all tasks have responses. Examples of these include tasks that
command a shaker, or management tasks.
a unique transaction-ID when invoking the reliable packet
delivery mechanism. The corresponding responses carry this
transaction-ID, enabling the correct de-multiplexing and as-
sociation of responses. Transaction-IDs have to be carefully
managed, since they persist across connections.
The stream delivery abstraction is implemented using neg-
ative end-to-end acknowledgements instead of a cumulative
acknowledgement as in TCP. This permits faster recovery of
multiple losses. In our current implementation, retransmission
buffers at the sending mote are stored in its EEPROM. For
improved performance, stream delivery relies on a limited
number of hop-by-hop retransmissions. The receiving end
ensures sequenced delivery to the application by buffering
packets. Our current implementation does not incorporate any
congestion control mechanisms, but rate limits the sender
to a configurable transmission rate. We have left congestion
adaptation to future work.
Stream delivery uses a connection establishment mechanism
very similar to that of TCP. However, because stream delivery
is fundamentally simplex, the connection establishment state
diagram is slightly simpler than that of TCP (omitted for
brevity) and requires fewer handshakes for connection estab-
lishment and teardown.
Finally, both stream and packet delivery work transparently
across the two tiers in NETSHM. Both types of reliable
delivery can traverse multiple hops on both tiers of the
network but there is almost no functional difference between
our implementations for the two layers, with one exception:
between neighboring motes our implementation uses MAC-
layers ACKs to perform hop-by-hop retransmissions, but be-
tween two upper-tier neighbors, we use a TCP “tunnel” for
implementation convenience.
E. Time Synchronization
NETSHM uses the FTSP [18] implementation in the
TinyOS tree for time synchronization, with some minor mod-
ifications for compatibility and improved accuracy. We also
changed the default clock frequency so that the clock wrap-
around time is sufficiently large, yet bounds time synchroniza-
tion error to within a few hundred microseconds, well within
one sample time (Section II-D).
V. EXPERIMENTAL RESULTS
We have implemented a modal frequency shift based dam-
age detection technique, and a mode-shape estimation based
damage localization technique on our NETSHM prototype.
We have tested these algorithms on a 48-inch scale model of
a 4-story building. We have also implemented a triggered data
collection application on a full-scale seismic test structure, to
get a sense of NETSHM performance under realistic wireless
conditions. This section describes these experiments.
A. Experimental Setup
The scaled building model (Figure 5) is 48 inches high,
with 1/2x12x18-inch aluminum plates which serve as floors
and are supported by 1/2x1/8-inch steel columns. Removable
Fig. 4. The seismic test structure
Fig. 5. The 48 inch scaled
model of a 4-story building
%task motes to sense and send data
%create a group for sensors
%create a group for actuators
% find the modal frequencies from all the samples
%read the originally stored modes
%detect possible damage
modes = findModes(samples)
load originalModes ;
;
shift = findModalFreqShifts(modes,originalModes);
function shift = getModalShiftsFromBuilding()
idSensors = NetSHMCreateGroup([16,7,13,14,5,2,4,3]);
idActuator = NetSHMCreateGroup([6]);
NetSHMCmdActuate(gidActuator,22);
samples = NetSHMGetSamples(gidSensors,20,200,1,4) ;
actuate after 22 seconds %
% 4000 samples at 200Hz along x axis starting 20 secs from now
Fig. 6. Sample code in matlab for detecting
damage in the structure
1
k
k
k
k
m
m
m
m
1
2
2
4
4
3
3
Fig. 7. The
lumped
mass spring
model of
the building
5.5 lbs/inch springs serve as braces between the floors of the
structure. These springs augment the stiffness between the
floors. Damages are induced by removing these springs from
the structure. Removal of each spring is estimated to bring a
reduction in stiffness by about 2.5% in that floor. The building
has four wirelessly controlled shakers built using off-the-shelf
components. These can be tasked via an attached Mica-Z mote
to deliver impulses to the top floor of the structure.
The seismic test structure (Figure 4) is a platform for
conducting seismic experiments on a full-scale realistic im-
itation of a 28’× 48’ hospital ceiling. The ceiling is complete
with functional electric lights, fire sprinklers, drop ceiling
installations and water pipes carrying water. Furthermore,
the ceiling is designed to support 10,000 lb of weight. The
entire ceiling can be subjected to uni-axial motion with a
peak-to-peak stroke of 10 inches, using a 55,000 lb MTS
hydraulic actuator having a ± 5 inch stroke. The hydraulic
pump delivers up to 40 GPM at 3000 PSI. The total weight
of the moving portion of the test structure is approximately
12,000 lb. Currently, the test structure requires a human-in-
the-loop to actuate the shaker.
Our NETSHM prototype runs on a hierarchical network
of PCs, Stargates and Mica-Z motes. We later describe the
detailed network configuration for each of our two structures.
Attached to the Mica-Z motes is a vibration card specially
designed for high-quality vibration sensing. The vibration card
can be programmed to sample at frequencies from 5Hz to
20KHz at 16 bits per sample and has a programmable anti-
aliasing filter to accommodate different sampling rates. The
16-bit ADC of the vibration card is controlled by an on-
board microprocessor, which in turn can be commanded by
the attached Mica-Z mote via a serial port. The stored samples
can be retrieved in one shot from the on-card 64K byte
SRAM by issuing commands over the serial port. We modified
the card firmware to support retrieval of blocks of samples
from the card’s RAM. This enabled us to conserve memory
on the Mica-Z. Finally, we attached highly sensitive tri-axial
accelerometers (dynamic range of -2.5g-2.5g, sensitivity in the
μg range), to the vibration card.
B. NETSHM Applications for Damage Detection and Local-
ization
We have implemented two SHM techniques, one for damage
detection and another for damage location, as NETSHM
applications. intent to devise novel SHM techniques. Rather,
our intent is to show how to take existing SHM techniques,
modify them to leverage local computation, and to understand
the ease of programming them using our NETSHM prototype.
Damage Detection: We have implemented a modal fre-
quency shift based damage detection application in NETSHM.
Figure 6 depicts the code for this application as an example
to demonstrate how an SHM engineer would write in matlab
using NETSHM. This application works as follows:
1) Actuate the structure and collect the structural response
histories from all the sensors.
2) Estimate the power spectral densities for each of the
collected structural response histories. We do this by first
estimating the auto-correlation of each history followed
by a Fourier transform of the autocorrelations. The
magnitude of the Fourier transform provides the power
spectral density.
3) Perform peak detection on the power spectral densities
of the time histories and select dominant peaks (greater
than a certain threshold of energy) to create a set of
modal frequencies for each structural response history.
4) The complete set of the modal frequencies is the union
of the modal frequencies discovered for each structural
response history.
In NETSHM, such a technique can be implemented using
the triggered model by tasking the actuators and sensors in
Step 1, then performing steps 2 through 4 on an upper-tier
node. When mote technology evolves to the point where it
becomes feasible to compute peaks in power spectral densities
at the motes, NETSHM can (transparently to the application)
push steps 2 and 3 to be computed locally on the motes.
Each mote would then transmit the modal frequency peaks
alone, resulting in significant communication energy savings.
We quantify the extent of this savings in a later section.
Damage Localization: The damage localization applica-
tion that we have implemented in NETSHM is that of Caicedo
et al. [35]. This technique is specifically designed for multi-
storied buildings and can localize the floors where the damages
have occurred. The scheme models a multi-storied building
as a mass spring system (Figure 7). The stiffness k
i
of each
spring is the combined stiffness of all the members of the i
th
floor and the mass m
i
is the entire mass of each floor. For our
4-story building model, the solution to finding the stiffness of
each floor is given by:
ψ j
= kμ j
(3)
ψ j
=




v
j1
v
j1
− v
j2
0 0
0 v
j2
− v
j1
v
j2
− v
j3
0
0 0 v
j3
− v
j2
v
j3
− v
j4
0 0 0 v
j4
− v
j3




(4)
k =

k
1
k
2
k
3
k
4

T
(5)
μ j
= λ j

m
1
m
2
m
3
m
4

(6)
Here, v
j
=

v
j1
v
j2
v
j3
v
j4

T
is the j
th
mode shape
induced in the structure and λ j
is the corresponding eigen-
value. This leads to the following system of equations:





ψ 1
ψ 2
.
.
.
φ l





=k





μ 1
μ 2
.
.
.
μ l





(7)
The stiffness is estimated as a least-mean-square fit solution
over all dominant modes in the structure. To estimate the mode
shapes we used the ERA algorithm [14]. This computation is
fundamentally centralized. As such, this localization technique
can be implemented using the triggered model by tasking
sensors and actuators in a manner similar to Step 1 of the
damage detection implementation, and performing the above
computations on an upper-tier node.
However, while our current implementation does not do this,
we believe this algorithm is amenable to an implementation
that uses local computation. Beck et al. [36] showed that
using auto-correlation of the impulse response rather than the
impulse response itself can lead to more robust estimates of
the mode shapes. This means that individual sensors can, in
theory, locally compute the auto-correlation of the impulse
response and only transmit the auto-correlation coefficients.
Below, we quantify the communication savings that such an
implementation would enable.
Detection and Localization Results
We deployed and tested these SHM applications on the
scaled building model. On this model, we deployed 8 Mica-Z
modes with vibration cards, and another Mica-Z mote attached
to our actuators. Two motes were deployed on each floor
of the building and each mote was attached to a tri-axial
accelerometer. This configuration is necessary since, for small
deformations each floor has three degrees of freedom (x, y
and θ ), and at least 2 accelerometers are necessary to estimate
these quantities. In our setup, accelerometers were placed at
two diagonally opposite corners of each floor. A preliminary
analytical evaluation revealed that the highest modes were
about 20Hz, so our SHM applications task the motes to sample
at 200 Hz (Section II-D).
The upper-tier network was formed by a PC and a Stargate,
the latter acting as a single gateway to the motes (we discuss
experiments with multiple gateways in Section V-C). The
PC ran our NETSHM applications and each application was
programmed to run a single test. In each application, sensors
were tasked to begin sampling at a specified time, and the
actuators were tasked to deliver an impulse about 1 seconds
after that. Each sensor collected the structural response for 50
seconds, then transmitted the data to the PC using our reliable
transmission and routing modules.
We report results obtained by running our NETSHM appli-
cations on ten different damage scenarios (see Table I). The
“undamaged” structure corresponds to the configuration witht
all the braces intact (case 0). For each application, we ran
each scenario five times, and present below the aggregated
results from these tests. Our repeated tests were designed to
illustrate the variability that can arise due to the effect of
ambient temperature and humidity of structural properties, and
the variability in sensor noise, actuator hysteresis etc. across
different invocations.
Damage Detection Results: In Table I, second column,
we list all the estimated modal frequencies encountered for
each configuration. The spectral resolution of our scheme
was 0.1Hz in our scheme and we found the estimated modal
frequenices to be consistant across all the trails for every
damaged case. Damages in a structure may result in a decrease
in modal energy at some frequencies, accentuate an originally
dormant mode and make it appear, or even make a mode
disappear entirely. Typically the higher the frequency of the
mode, the higher the shift in frequencies after damage. As seen
in Table I the modal frequencies in bold that exhibit a change
from the common damage case i.e., Case 0 are depicted in
bold. Our implementation is able to detect changes at least in
one mode for every test case. This is a very encouraging result
which validates that, at least for test structures, NETSHM
can be used to rapidly prototype accurate damage detection
algorithms. This is important — even if NETSHM is not
used on real structures, a lot of structural engineering research
uses actual experiments on test structures and a platform like
NETSHM has the potential to spur the research conducted by
that community.
Damage Localization Results: Columns 3-6 in Table I
depict the calculated average loss of stiffness for each of
the four cases over several tests. As seen from Table I each
of the damages were accurately localized as reflected by
the significant loss of stiffness (about 5% and 10%) in the
corresponding floors and a small variation in the rest of the
Fig. 8. Layout of the motes in the seismic
structure
0 500 1000 1500 2000 2500 3000
0
50
100
150
200
250
Number of samples received
Latency (sec)
node2
node3
node4
node5
node7
node8
node9
node10
node12
node13
node14
node16
Fig. 9. Latencies measured in the seismic-test
structure deployment
0 500 1000 1500 2000 2500 3000
0
50
100
150
200
250
300
Number of samples received
Latency (sec)
node2
node3
node4
node5
node7
node8
node9
node10
node12
node13
node14
node16
Fig. 10. Latencies measured when one Star-
Gate was unplugged during the experiment
TABLE I
RESULTS OF THE DAMAGE DETECTION AND LOCALIZATION ALGORITHMS
Damaged test case Modal frequencies Average estimated %age loss of stiffness
found in Hz floor 1 (k
1
) floor 2 (k
2
) floor 3 (k
3
) floor 4 (k
4
)
C0 : all springs intact 2.6, 7.8, 12.0, 14.9 0.0 0.0 0.0 0.0
C1 : 2 springs removed from floor 4 2.6, 7.4, 7.7, 11.9, 14.8 0.1 0.073 -0.251 4.371
C2 : all springs removed from floor 4 2.6, 7.7, 11.8, 14.8 0.24 0.13 -0.18 8.814
C3 : 2 springs removed from floor 3 2.6, 7.8, 12.0, 14.7 -0.23 -0.05 4.486 -0.15
C4 : all springs removed from floor 3 2.6, 7.4, 7.7, 12.0, 14.6 0.99 -0.22 8.74 -0.31
C5 : 2 springs removed from floor 2 2.6, 7.8, 11.9, 14.8 0.68 4.734 -0.1 0.3
C6 : all springs removed from floor 2 2.6, 7.8, 11.7, 14.7 0.71 9.54 -0.28 -0.37
C7 : 2 springs removed from floor 1 2.6, 7.7, 11.9, 14.8 5.85 0.04 -0.56 -0.14
C8 : all springs removed from floor 1 2.6, 7.4, 11.9, 14.8 10.39 -0.05 -0.67 -0.13
C9 : all springs removed from floors 3 and 4 2.6, 7.7, 11.7, 14.5 1.13 -0.28 11.51 6.87
C10 : all springs removed from floors 1 and 4 2.6, 7.0, 7.4, 7.7, 11.6 14.7 -0.46 -0.78 9.21
TABLE II
POTENTIAL COMMUNICATION SAVINGS FROM USING LOCAL
COMPUTATION
SHM Application Actual Messaging Local Computation
Cost (in Pkts) Cost (in Pkts)
Damage Detection 10000 8
Damage Localization 10000 80
floors (about 1%). The fact that we are able to actually localize
damage on the appropriate floors even with multiple damaged
locations (cases 9 and 10) is highly encouraging both as a
validation of NETSHM as well the applicability of dense
sensing for this application domain.
Finally, the table II quantifies the communication savings
that could have been obtained if local computation were used
in our NETSHM prototype. We computed these numbers by
using the raw sensor received during our two experiments,
then computing the number of packets necessary to transmit
the modal frequencies (for damage detection), or the auto-
correlation coefficients (for localization). Notice the three
orders of magnitude reduction in communication cost.
C. NETSHM at Scale
Finally, we deployed a 14-mote, 2-stargate NETSHM pro-
totype on the seismic test structure shown in Figure 4. The
NETSHM application was implemented on a laptop. Figure 8
shows the layout of our network and also depicts the topology
for the duration of the experiment. In the figure, dark solid
arrows depict dominant links (links over which more than
40% of the packets were transmitted) to a Stargate, while dark
dashed arrows depict the less frequently used links. Motes 9
and 13 used both the Stargates to send a significant fraction
of the packets at different times during the experiment, while
the other nodes were connected to only one of the Stargates
for most of the time. The gray dashed arrows depict the
links used in the lower-tier i.e., multi-hop routes among the
Mica-Z motes. The bi-directional arrows indicate that the link
shifted its direction during the experiment. The laptops and
the StarGates communicated to each other using 802.11 radio
in adhoc mode.
We cannot currently induce damage on this large structure,
so we tested a NETSHM application that collects the impulse
response of the entire structure. The application tasked the
motes to collect data for 1 minute at 50Hz (3000 samples)
while the structure was manually actuated to generate im-
pulsive excitation. We time-stamped every sample as it was
received at the application and measured latency as the time
elapsed since the sensors completed the data. Figure 9 depicts
the latencies incurred for transporting samples from each of
the motes reliably to the PC. As one would expect, the latency
increases linearly for successive samples, since all motes were
rate-limited to 2 pkts/sec (each packet carried 8 samples).
We also conducted a test to demonstrate the robustness of
the system by failing one of the Stargates. The latency of
route recovery is depicted in Figure 10. Most motes incurred
an increase in latency of about 30-100 seconds, some of
which can be attributed to the route update interval, and some
to packet loss recovery times. Eventually, all samples were
recovered correctly at the base station.
VI. CONCLUSIONS AND FUTURE WORK
This paper is an attempt to move away from a vision
of sensor networks as being necessarily application-specific.
Our design and validation of NETSHM suggests that, at
least for a class of high-data rate applications, there exists
an architecture that presents a fairly generic programming
abstraction and where many systems components (routing,
reliable transmission etc.) can be reused across applications.
We conjecture that the NETSHM architecture might be more
broadly applicable to sensor networks in general, but have
deferred an examination of this question to future work.
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Krishna Kant Chintalapudi, Jeongyeup Paek, Omprakash Gnawali, Tat Fu, Karthik Dantu, John Caffrey, Ramesh Govindan, Erik Johnson. "Structural damage detection and localization using NetSHM." Computer Science Technical Reports (Los Angeles, California, USA: University of Southern California. Department of Computer Science) no. 866 (2005).

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Creator Caffrey, John (author), Chintalapudi, Krishna Kant (author), Dantu, Karthik (author), Fu, Tat (author), Gnawali, Omprakash (author), Govindan, Ramesh (author), Johnson, Erik (author), Paek, Jeongyeup (author)

Core Title USC Computer Science Technical Reports, no. 866 (2005)

Alternative Title Structural damage detection and localization using NetSHM (title)

Publisher Department of Computer Science,USC Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, California, 90089, USA (publisher)

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Format 12 pages (extent), technical reports (aat)

Language English

Unique identifier UC16269198

Identifier 05-866 Structural Damage Detection and Localization Using NetSHM (filename)

Legacy Identifier usc-cstr-05-866

Format 12 pages (extent),technical reports (aat)

Rights Department of Computer Science (University of Southern California) and the author(s).

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Source 20180426-rozan-cstechreports-shoaf (batch), Computer Science Technical Report Archive (collection), University of Southern California. Department of Computer Science. Technical Reports (series)

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Repository Name USC Viterbi School of Engineering Department of Computer Science

Repository Location Department of Computer Science. USC Viterbi School of Engineering. Los Angeles\, CA\, 90089

Repository Email csdept@usc.edu

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Title Computer Science Technical Report Archive

Description Archive of computer science technical reports published by the USC Department of Computer Science from 1991 - 2017.

Coverage Temporal 1991/2017

Repository Email csdept@usc.edu

Repository Name USC Viterbi School of Engineering Department of Computer Science

Repository Location Department of Computer Science. USC Viterbi School of Engineering. Los Angeles\, CA\, 90089

Publisher Department of Computer Science,USC Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, California, 90089, USA (publisher)