wireless intelligent sensor and actuator network â€“ a scalable

Wireless Intelligent Sensor and Actuator

Network – A Scalable Platform for

Time-synchronous Applications of

Structural Health Monitoring

Edward Sazonov,* Vidya Krishnamurthy and Robert Schilling

Department of Electrical and Computer Engineering, Clarkson University

Potsdam, NY, USA

Wireless sensor networks have attracted attention as a possible solution for applications

of periodic and continuous structural health monitoring. Ensuring synchronous data acqui-

sition across wireless nodes in large networks of sensors spatially distributed on a structure

is of critical importance for many methods of structural health monitoring, especially those

based on analysis of vibration. In this article we present a novel Wireless Intelligent Sensor

and Actuator Network (WISAN) addressing the issue of scalability for applications of struc-

tural health monitoring. We also present a novel time synchronization algorithm that can

keep the synchronization error between any number of globally distributed sensors nodes

less than �23 ms. We show proof of stability for the time synchronization algorithm.

We validate WISAN in laboratory experiments, testing the actual time synchronization

between randomly selected sensors in a complex network. Finally, we validate WISAN in

a field experiment by reconstructing mode shapes of a highway bridge.

Keywords wireless sensor networks � modal analysis � time synchronization.

1 Introduction

With the need for reduction in size and cost of

structural health monitoring (SHM) systems, wire-

less sensor networks (WSNs) are becoming increas-

ingly popular in these application areas [1–13]. A

monitoring system using sensors placed at various

locations on the structure is needed to monitor the

health of civil structures such as bridges. The eco-

nomic losses associated with disasters are much

higher as compared to the cost for monitoring the

structure’s health and performing early repairs

avoiding major calamities [14]. Among the different

available damage detection techniques, the class of

vibration-based damage detection techniques is one

of the most popularly used methods to monitor the

condition of structures [15–18]. As a specific case of

vibration techniques, mode shape-based damage

detection usually involves comparing specific

properties of both damaged and undamaged

mode shapes and determining locations with the

pronounced difference in properties [19–21].

*Author to whom correspondence should be addressed.

E-mail: [email protected]

Figures 1 and 6–9 appear in color online: http://shm.sagepub.com

� The Author(s), 2010. Reprints and permissions:

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Vol 9(5): 465–12

[1475-9217 (201009) 9:5;465–12 10.1177/1475921710370003]

465

Thus, the first step for damage detection is modal

identification and reconstruction of mode shapes.

A monitoring system using time-synchronized sen-

sors placed at various locations on the structure is

needed to acquire vibration response. The task of

vibration acquisition poses additional requirements

for the WSN, specifically, need for time-synchro-

nized acquisition among sensors; lossless and pref-

erably real-time data transmission; scalability to

various structure sizes and number of deployed sen-

sors. Wireless Intelligent Sensor and Actuator

Network (WISAN) has been developed to address

these issues and support arrays with large number

of heterogeneous sensors.

Time synchronization in WSNs has been an

important concern that restricted the application

of these networks in SHM applications. In our pre-

vious research [22] we have shown that for the

range of natural frequencies typical of a highway

bridge, synchronicity on the order of microseconds

is required between the wireless sensors to main-

tain signal acquisition errors on the level compara-

ble to sensor noise. Consider a network with each

wireless sensor in the network has its own hard-

ware oscillator-based clock providing timing sig-

nals. Due to varying oscillator frequencies, the

clocks on the sensors drift with respect to each

other and have to be continuously synchronized

with each other in order to maintain a common

time. While a number of methods have been devel-

oped and tested in this direction [23–27] most of

them were limited to simulation studies. As one of

the notable practical results, time synchronization

within 0.1ms [25] has been achieved using global

beacon synchronization where each wireless sensor

receives the same beacon ping and sets its internal

clock to 0. Another practical implementation of a

sensor network [28], which used a beacon-based

synchronization method, was tested on a highway

bridge in South Korea. The sensors were initially

synchronized to a 20 ms inter-sensor precision, but

accumulated up to a 5ms delay in a 6-s period.

While the complete review of available synchroni-

zation mechanisms goes beyond the scope of this

article, existing wireless networks presented in

[2–13,29–32] do not completely satisfy all the

requirements (scalability with structure size, data-

stream reliability, and time synchronization) posed

by SHM applications. Hence, there is a need for a

WSN platform that can scale up or down depend-

ing on structure size while keeping the sensors

tightly synchronous.

Satisfying both the scalability and the time syn-

chronization requirements is a challenging prob-

lem. For example, global beacon synchronization

can keep the time synchronized between sensors

within a desired upper bound of a few microsec-

onds, but this method does not scale well with the

size of the network. Indeed, presence of a common

beacon assumes that all sensors share the band-

width of a frequency channel. For applications of

vibration acquisition the channel capacity can be

quickly exhausted by a few sensors even if use of

the bandwidth is optimized by a reservation sched-

uler [33]. To resolve this dilemma we propose a

hierarchical architecture in which beacon synchro-

nization is used for local clusters of wireless sensors

and spatially distributed clusters are synchronized

by using GPS time reference.

In this article we present a detailed description

of the WISAN network architecture; the time syn-

chronization algorithm developed and imple-

mented along with proof of stability; and

laboratory and field validation tests performed

using the network. WISAN hardware and software

is discussed in Section 2. Section 3 highlights the

time synchronization algorithm implemented in

WISAN and establishes theoretical synchroniza-

tion limits. Section 4 describes the laboratory and

field tests conducted on WISAN. Results are pre-

sented in Section 5, followed by the Discussion and

Conclusions.

2 The WISAN Platform

WISAN hardware platform was built around

the ultra-low power MSP430 microcontroller

MSP430F1611 from Texas Instruments [34] and

CC2420 radio transceiver from Chipcon (now

also Texas Instruments) [35]. WISAN is fully com-

patible with IEEE 802.15.4 and can be utilized

worldwide in the 2.4GHz ISM frequency band

and coexist with Wi-Fi and other devices.

Detailed description of WISAN hardware can be

found in [36]. The network hardware is built using

two types of devices: The Coordinator and The

Wireless Sensor.

466 Structural HealthMonitoring 9(5)

The Coordinator (Figure 1(a)) maintains a

cluster of sensor nodes: sends commands, receives

and stores data, processes and/or forwards data to

a computer via a USB link. An input for the pulse-

per-second (PPS) signal from a GPS receiver

enables hardware clock synchronization with a

GPS time reference. The wireless sensors

(Figure 1(b)) are used for acquiring data and deliv-

ering them to a Coordinator. Each wireless sensor

is equipped with the peripheral circuitry for inter-

facing of sensors and actuators. The peripheral cir-

cuitry consists of a variety of components that may

be selected on application needs. A low power 3D

MEMS accelerometer (LIS Accelerometer [37]) is

used as internal on-board sensor. A gain and offset

compensation stage is provided to improve the res-

olution. Signal conditioning is done using a fifth-

order low-pass filter with cutoff frequency config-

urable over the wireless link. The noise resolution

of the accelerometer in the frequency band of

100Hz is 0.5mg. An optional buffered output

terminal allows sourcing of control and excitation

signals that can be used as actuation signals. A

connector is provided for interfacing an external

acceleration sensor. Differential signal condition-

ing circuitry is also provided with a gain of 1000

to interface high precision strain sensors. The wire-

less sensors are enclosed using commercially avail-

able NEMA4 enclosures. All circuits feature

shutdown inputs, so a part or the whole wireless

device can be put into low-power sleep mode.

WISAN software has been developed to effi-

ciently work with low power hardware and satisfy

the requirements of a SHM system. The network

protocol is the key ingredient of any networked

system, since it is responsible for reliable, error-

free, and on time data delivery. The medium

access protocol network software is based on the

low-latency, low-power IEEE 802.15.4 protocol,

which has emerged as a new standard for low

rate wireless personal area networks (LR-PANs)

[38] and hence chosen for WISAN. Since the

IEEE 802.15.4 protocol only provides the lower

layers, that is, the physical layer and the medium

access Layer, the software development for the net-

work also focused on developing application and

transport layers of the network. Reliable data

delivery and substantially reduced power con-

sumption were achieved by a reservation-based

scheduler implanted at the Coordinator level [33].

The network architecture of WISAN is a clus-

ter tree. This choice is motivated by the fact that

wireless sensors remain static on the structure and

thus allow utilization of energy efficient hierarchi-

cal cluster architecture where most of the commu-

nications between sensor nodes and dedicated

cluster heads (Coordinators) is performed in

single hop. A diagram of a WISAN network is

shown in Figure 2. A certain number of sensors

(within the maximal available bandwidth) are asso-

ciated with a particular Coordinator on one of the

16 frequency channels. The spread of a cluster is

limited by the transmission range of the sensors

(�30m ) and a maximum of 16 clusters can be

co-located in a given area enabling dense place-

ment of sensors on a structure. Each Coordinator

forwards the data collected within the cluster to a

TCP server which can be accessed by a LabView

client over a high-capacity network link (e.g., wire-

less or wired Ethernet, cellular link). The proposed

network architecture is highly scalable and allows

building networks of large size (hundreds or even

thousands of nodes) distributed over large

structures.

3 Time Synchronization Algorithms

Multiple clusters of wireless sensors covering

the monitored structure need to be closely

(a) (b)

Figure 1 (a) WISAN Coordinator, (b) WISAN sensor node.

Sazonov et al. Wireless Intelligent Sensor and Actuator Network 467

synchronous to be used with vibration-based

damage detection. The synchronization should

not depend on the network size (e.g., number of

nodes in the network or physical area spanned by

the network). In the proposed hierarchical network

shown in Figure 2, each wireless sensor connects to

a Coordinator through a low-latency single-hop

connection. The Coordinators are interfaced to

the Application client via a high-bandwidth TCP

link. Thus, time synchronization in the network

has to be maintained at two levels:

(1) Within a cluster (all sensors that belong to a

Coordinator), and

(2) Between clusters (among all Coordinators).

3.1 Time Synchronization Between Sensors

Within a Cluster

At the lower hierarchical level, sensors in the

same cluster are periodically synchronized to the

beacons emitted by the Coordinator. Each time a

beacon packet arrives, the radio chip generates a

pulse, which in conjunction with a capture-and-

compare module of the microcontroller, corrects

the internal timer by tSENSORd ¼ tTMR � tBCN

(Figure 3) to compensate for accumulated time dif-

ference. The 8MHz crystal oscillator used for the

sensors and the Coordinators has a frequency tol-

erance of �30 ppm. Hence, the maximum time dif-

ference between a sensor node and its Coordinator

operating at 8MHz would be 60 ms/s. Since in

WISAN the beacons are emitted every 245.76ms

(beacon order¼ 4) the maximal drift between a

Coordinator and a sensor synchronizing on beacons

is less than �15ms (Tsensormax ). This is the maximum

amount of accumulated time error that exists

between any two wireless sensors in the cluster.

3.2 Time Synchronization Between

Coordinators

Each cluster will be synchronous with the

global time if all Coordinators emit beacons at

the same time. A GPS receiver can be used to pro-

vide reference time for one or more Coordinators.

Most GPS receivers are capable of generating a

hardware PPS signal, which is synchronized to

Coordinated Universal Time (UTC) within

�100 ns [39]. It is not feasible to use GPS for indi-

vidual synchronization of every node in the net-

work due to power and visibility considerations.

A GPS receiver takes a significant amount of

power to operate and would reduce operational

time of the sensor nodes before requiring battery

replacement. Most importantly, GPS needs a clear

view of the sky, which is impossible for most

TCP server

Application client: Control and data collection

WISANsensors

Long-range communications

A cluster

TCP link

Coordinator 1

Coordinator 2

Coordinator 3

GPS

TCP link

TCP server

Serial connection

Coordinator 4

Coordinator 5

Coordinator 6

GPS

Short-range communications

Figure 2 WISAN network topology.


sensors. However, the Coordinators can be located

anywhere on the structure within the transmission

range of the sensors and thus could easily utilize

GPS time reference. The PPS signal can be used to

synchronize spatially distributed Coordinators by

using it in the same manner a beacon is used within

a cluster (Figure 4). The Coordinators run on an

8MHz clock or 0.125 ms clock interval. Emitted

beacons are timed by a sequence of two timers:

Timer B and MAC timer. Under conditions of per-

fectly stable crystal frequency the MAC timer

counts 3125 times in 1 s (or 768 times between con-

secutive beacons). Given the maximum clock drift

of �30ms in one second [40], if the Coordinator is

slow relative to UTC then upon arrival of a PPS

signal the MAC timer count will be þ3124 (relative

to the value at arrival of the previous PPS signal)

and Timer B count will be less than the maximum

8 MHzOscillator

Timer B(0–2559)

0.125µs MAC timer(0–767)

Capture module

320 ms

GPSTPPS=1stPPS

tTMR

Dt = tTMR − tPPS

Synchronize with PPS:

•ticks of MAC timerAdjust Timer B by a every Bi

• Reset Timer B every TPPS (correct by τ ′ i =τ i − (Di −Di−1 + ε i ) ).

• Avoid double MAC timer update in case of fast Timer B.

RadioEmit beacon

245.76ms

Figure 4 Time Adjustments in the Coordinator time synchronization algorithm.

8 MHzoscillator

Timer B(0–2559)

0.125µs

ADC

Programmabledivider

Capturemodule

320 ms

Samplingtrigger

Sensor

RadioA beacon from

Coordinator every245.76ms

Correct timer B by

tdSENSOR= tTMR − tBCN

tTMR

tBCN

Figure 3 Time adjustments in the sensor node.


(2560) but above the half count of 1280. If the

Coordinator is fast, the Timer B counter will be

in the range of lower half (0–1280) with a relative

MAC timer count of þ3125. If Timer B is reset to

zero and the MAC timer set to a relative count of

þ3125 upon capturing a PPS time reference, the

Coordinators will be almost perfectly time syn-

chronous, however, only at that moment in time.

The problem with this approach is that adjusting

the time difference in one step can create up to

120ms time jumps due to time differences accumu-

lated between consecutive PPS pulses. Such a large

degree of asynchronicity is unacceptable for appli-

cations of modal identification. To avoid accumu-

lation of the time errors the clock drift can be

compensated by making a number of small adjust-

ments distributed across the period TPPS. The

upper bound error between consecutive MAC

timer ticks can be minimized and kept within rea-

sonable bounds.

A more formal presentation of the procedure is

as follows. At any given time instant t, let the

actual clock drift given as �(t) such that

�(t)¼ �(t0)þR � (t� t0)¼ �(t0)þDt where t is the

current time, �(t0)� is the time offset at the last

measurement update t0, R is the frequency toler-

ance of the crystal and Dt is the drift at time t with

respect to t0. In WISAN, R can have a maximum

difference between two Coordinators of 60 parts-

per-million (PPM) due to the tolerances of the

crystal oscillator. If not corrected, the resulting

errors can accumulate to the order of a few seconds

per day. The time synchronization algorithm esti-

mates �(t0) and R at regular intervals Tpps (1 s) and

adjusts the clock to minimize �(t) in the future. For

any PPS interrupt i�1 from the GPS receiver, all

Coordinators set Timer B to zero (�i�1¼ 0). The

accumulated time difference on the next, i-th inter-

rupt is given by �i� �i�1¼R � (Tpps)¼Di.

Since Di is proportional to the crystal tolerance

R it is safe to assume that under most realistic con-

ditions value Di remains approximately constant

over the period of Tpps. Then Di can be compen-

sated over the next Tpps period in a fixed amount

A of time adjustments (increments/decrements)

of fixed size �, thus keeping the maximum synchro-

nization error significantly less than Di. The

number of adjustments Ai and adjustment period

Bi are calculated as: Ai ¼ Int Di=��

, where

Bi ¼ Int 3125=Ai

� �and Int . . .h i is an operator that

rounds-off the division to the lower integer (all

timers are integer devices). The adjustment period

B1 is further used to calculate the actual number of

adjustments made for Tpps¼ 1 s, A0i ¼ Int 3125=Bi

� �.

Hence, the errors induced due to the fact that

the Timer B can be adjusted only in integer incre-

ments/decrements would be "i ¼ Di � A0i� where

A0i is the number of adjustments and � is the

size of the adjustments. At the next PPS inter-

rupt the difference between the UTC refer-

ence and internal clock would be, �iþ1 ¼

Diþ1 �Di þ "i ¼ Diþ1 � A0i�. The value of A

needs to be adjusted if "i4�, where � is the error

correction threshold which is the maximal error

obtained by incrementing or decrementing Biþ1

one step above or below Bi.

The practical implementation of the Coordina-

tor time synchronization algorithm consists of two

independent parts: parameter estimation and timer

adjustment. The parameter estimation part is

shown in Figure 5. Every Tpps the time correction

parameters Ai and Bi are recalculated according to

the observed error. The time adjustment part is

shown in Figure 4. Timer B is incremented of dec-

remented by � (4 timer ticks or 0.5 ms) every Bi ticks

of MAC timer. At every TPPS the Timer B

1. Initialize variables and parameters

2. At PPS interrupt i do

a. Compute

b. if ti > Λ

else

i = i +1

Ai = Ai –1Bi = Bi –1 Ai = Ai –1

t0 = 0, A0 = 0, B0 = 0,D0 = 0, 0 = 0, Λ = 0, i = 0,a = 0.5 ms, N = 3125

§

Di = tTMR – TPPSti = Di – Di –1 + i –l

§

Ai = Int ⟨Di /a⟩

Ai′ = Int ⟨N /Bi⟩Bi = Int ⟨N /Ai⟩

i = Di –Ai′a

§

i = Di –Ai a,§= =i = Di –Ai a

§− −

Λ = max { , }§=i

§

i

=Ai = Int ⟨N /(Bi + 1)⟩, Ai = Int ⟨N /(Bi – 1)⟩

−

−

Figure 5 Parameter estimation in the Coordinator timesynchronization algorithm.


is corrected by a reset to zero to avoid accumula-

tion of the integer arithmetic error �0i ¼

�i � ðDi �Di�1 þ "iÞ. Since such a reset may cause

an automatic increment of the MAC timer, a cor-

rection is made to avoid an update of the MAC

timer in case Timer B is faster than TPPS.

The stability of the proposed Coordinator time

synchronization algorithm can be proven under

conditions of slowly changing synchronization

error. When Diþ1 � Di and "ij j � ", then

�iþ1�� " and the algorithm keeps the error clock

offset error within the minimal computation bound

and hence stable.

Given the crystal frequency stability of

�30 ppm, a maximum drift on each Coordinator

is �30 ms, the maximum time difference accumu-

lated between two Coordinators is �60ms/s (480

timer B ticks). To find the bound " on this error,

numerical computation of the error "i was per-

formed for a range of accumulated differences

between 0 and 120ms. Figure 6 shows that the

error due to integer round-offs lies between 0 and

8 ms suggesting an upper bound " of �8 ms. Thus,

using the time synchronization algorithm, all

Coordinators in the network run on virtually iden-

tical time synchronized within an accuracy of �8 ms

(TPANmax ).

The time synchronization mechanism in

WISAN relies on a simple hierarchical organiza-

tion where synchronous operation of the global

network is achieved by keeping wireless sensors

tightly synchronized to a Coordinator and by

keeping all Coordinators tightly synchronized to

GPS time references. Overall, the maximal syn-

chronization error among sensors in WISAN

network (TWISANmax ¼ TPAN

max þ Tsensormax ) is less than

�23ms. If, however, a crystal of �10 ppm tolerance

is used in WISAN wireless nodes, the maximal syn-

chronization error using the above algorithm

would be less than �6 ms.

4 Experimental Analysis

4.1 Laboratory Tests

Experiments were conducted to verify the per-

formance of the proposed time synchronization

algorithm. The first experiment tested synchroniza-

tion of sensors within a cluster. A sinusoidal signal

from a signal generator was physically wired to

two wireless sensors associated with the same

Coordinator. Time differences in sampling directly

correspond to a shift in the phase of the acquired

sinusoidal signal. An FFT routine is thus applied

to determine the amplitude and phase of the

received data. Let f1(t) and f2(t) be the two received

signals such that f1ðtÞ ¼ sinð!0tþ �1Þ and

f2ðtÞ ¼ sinð!0tþ �2Þ. The respective Fourier trans-

forms given as F1(x) and F2(x) have peak values at

frequency !0, with corresponding phase difference

�1 � �2. The time difference of the wireless sensors

is then calculated from the phase difference

�12 ¼ �1 � �2ð Þ=!0

�� .The second experiment tested accuracy of time

synchronization of WISAN network on the

Coordinator level of hierarchy. A hierarchical net-

work shown in Figure 2 was built with two sepa-

rate computers serving 3 Coordinators each. The

computers were connected by a TCP link over

wired Ethernet. Application client software was

run on one of the computers. Each Coordinator

serviced a cluster of 8 sensors for the total of 48

wireless sensors in the network. Two GPS receivers

were used to provide synchronization pulses to

three Coordinators each. A sinusoidal signal was

wired to the external sensor port on six of ran-

domly selected wireless sensors on different clusters

while the rest of the sensors were transmitting

acceleration data. Asynchronization between the

wireless devices was measured using the phase dif-

ference between the captured sinusoidal signals.

Delay value Di (ms)

0–1

0

1

2

3

Tot

al in

tege

r ar

ithm

etic

err

or e

A (

ms)

4

5

6

7

8

20 40 60 80 100 120

Figure 6 Error due to integer arithmetic in incrementing/decrementing the timers.


In the third laboratory experiment we tested

the validity of the data acquired by distributed

wireless sensors. WISAN has been used to perform

mode shape reconstruction tests on a

100’’� 1’’�½’’ standard aluminum (6061) beam

using 21 wireless sensors associated with 3

Coordinators. The mode shapes were recon-

structed by output–output modal analysis using

ARTeMIS [41] and compared to the analytical

model as well as a finite element model processed

in Visual NASTRAN. This experiment has been

previously reported with the detailed description

found in [22].

4.2 Field Testing Using WISAN

To show practical applicability of the time-syn-

chronous and scalable architecture of WISAN to

applications of SHM, WISAN was tested on mode

shape extraction from ambient vibrations of a

bridge. The test bridge is an integral abutment,

single span (19.8m or 65 feet), four steel girder

bridge located in the Town of Lisbon, NY

(Figure 7(a)). The bridge is located on a rural

county road with very low traffic volume. The

underside of the bridge is easily accessible from

the ground.

The bridge was instrumented by 44 WISAN

sensors installed as 11 sensors per each of the

four girders (Figure 8). The sensors were clamped

on the bottom of the girders (Figure 7(b)) and

communicated with the Coordinator station

placed under the bridge. The Coordinator station

consisted of six Coordinator nodes connected to a

laptop computer. GPS was used to provide PPS

PanCoordinator station

Wireless sensors

Girder 1

Girder 2

Girder 3

Girder 4

Figure 8 Layout of the bridge test setup.

(a) (b)

Figure 7 (a) View of the test bridge, (b) sensor attachment.


synchronization signal to all Coordinators.

Control and data retrieval from Coordinators

was performed through a TCP server by a

LabView-based client application. The sensors

formed six network clusters on six independent

frequency channels. Acceleration data from each

wireless sensor were sampled at the rate of

240.385Hz and resolution of 14 bits. Traffic pass-

ing over the bridge was the source of bridge exci-

tation. A number of experiments were conducted

for data recordings of 30 s, 2min, 5min, and

10min in duration. The cutoff of frequency of

the programmable low-pass filter was varied

between 40Hz and 100Hz to filter out high fre-

quency noise components.

5 Results

The laboratory time synchronization tests

showed that the measured time synchronization

error between the wireless nodes lies well within

the predicted limits. In the experiment that tested

sensor synchronization within a cluster the mea-

sured error for a set of five experiments varied

between 2.6 and 10.4ms with the mean of 5.1 ms

and standard deviation of 3.1 ms. These results con-

firm the upper bound on time error within a cluster

as �15ms calculated in Section 3.

The test of time synchronization between wire-

less sensors of a spatially distributed network syn-

chronized by GPS time reference established an

error range from a minimum of 0.1 ms to a maxi-

mum of 12ms with the mean of 6.73ms and stan-

dard deviation of 3.4 ms for a set of 5 experiments.

These tests confirm the capability of WISAN sen-

sors to continuously sample time synchronous data

(with a precision of �23 ms) in network configura-

tions that can be scaled to cover large structures.

Results from the reconstruction of the alumi-

num beam mode shapes have been reported in [22]

and do not fit within the space allotted for this

article. Overall, measured natural frequencies and

reconstructed mode shapes for the beam fit well

with analytical equations. The average coefficient

of correlation for the first three modes (computed

over 12 experiments) was 0.944, indicating correct

extraction of experimental mode shapes by

WISAN system. The beam experiments confirmed

the validity of the data acquired by WISAN and

correctness of the methods used for modal identi-

fication from vibration data.

Results of the field experiments demonstrate

the ability of WISAN to reconstruct mode shape

from complex structures using the proposed scal-

able and time-synchronized architecture. We have

identified several mode shapes at 9.155, 10.97,

26.23, 32.75, and 53.65Hz (Figure 9). Vibration

response of the bridge had a peak amplitude of

25mg in the range of 8–12Hz, which is rather

low for the MEMS accelerometers used as sensors.

Nevertheless, reconstructed mode shapes are clear

and easily identifiable.

(a) (b)

(c) (d)

Figure 9 Mode shapes identified in the test bridge: (a) 9.155 Hz, (b) 10.97 Hz, (c) 32.75 Hz, (d) 53.65 Hz.


6 Discussion

Our goal is creation of a scalable WSN in

which every node remains time-synchronous to

all other nodes in the network regardless of how

many nodes form the network or how large of an

area the network covers. These two properties are

critical for any method of SHM that involves

modal identification of structures. The proposed

scalable and time-synchronous WISAN network

is based on hierarchical architecture in which wire-

less sensors with limited transmission range form a

cluster and synchronize with a Coordinator by

using the beaconing mechanism. Several clusters

can coexist within the same area by occupying dif-

ferent frequency channels. This allows forming

very dense sensor installations. All Coordinators

in the network are synchronized to a reference

GPS signal, thus ensuring almost perfect time syn-

chronicity of every wireless node.

The results of laboratory tests show that the

actual synchronization error is well within the

bounds (�23ms) predicted by the analysis. In

theory, because of the GPS synchronization of

the Coordinators, even very large networks can

operate with maximum synchronization errors

less than �23 ms. This capability could be useful

for applications on bridges with lengths of kilome-

ters. Even tighter synchronization can be achieved

by using better crystals (�6 ms using a �10 ppm

crystal) and newer microcontrollers from TI that

have lower interrupt latency.

Results of reconstructing beam mode shapes

[22] referenced here show that WISAN provides

valid data and corresponds well with the com-

monly used methods of modal identification.

Results of bridge testing confirm conclusions

of laboratory tests and show that WISAN can be

deployed for practical applications of monitoring

highway infrastructure. The reconstructed mode

shapes could only be obtained under conditions

of tight sensor synchronization [22]. It should be

noted that we did not attempt finite element

modeling of the bridge used in field experiments

due to labor intensity of such modeling.

However, the reconstructed mode shapes behave

as expected from a structure of the given size and

geometry. Thus, the field experiment successfully

demonstrated the applicability of WISAN to

testing of the real structures, which is a step

toward practical applications of wireless SHM.

7 Conclusions

In this article we presented a scalable, hierarchi-

cal, time-synchronous wireless sensor network,

WISAN. The scalability and time synchronization

aspects of the network architecture have been

described in detail. We presented a novel time syn-

chronization algorithm that was implemented in

WISAN and demonstrated the algorithm’s stability.

Various laboratory and field validation tests have

been presented and the results show that the pro-

posed hierarchical network architecture can satisfy

both scalability and time synchronization require-

ments. This scalable network architecture can be used

to create time-synchronous sensor networks for appli-

cations of SHM-based on analysis of vibration.

Acknowledgment

The authors would like to acknowledge support provided

by New York State Energy Research and Development

Authority.

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