Wireless Intelligent Sensor and Actuator Network - Semantic Scholar

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 acquisition 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 structural 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, wireless sensor networks (WSNs) are becoming increasingly 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 economic 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

ß The Author(s), 2010. Reprints and permissions: http://www.sagepub.co.uk/journalsPermissions.nav Vol 9(5): 465–12 [1475-9217 (201009) 9:5;465–12 10.1177/1475921710370003]

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Thus, the first step for damage detection is modal identification and reconstruction of mode shapes. A monitoring system using time-synchronized sensors 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-synchronized acquisition among sensors; lossless and preferably real-time data transmission; scalability to various structure sizes and number of deployed sensors. 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 previous 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 maintain signal acquisition errors on the level comparable to sensor noise. Consider a network with each wireless sensor in the network has its own hardware oscillator-based clock providing timing signals. 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 developed 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 0.1 ms [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 5 ms delay in a 6-s period. While the complete review of available synchronization 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, datastream reliability, and time synchronization) posed by SHM applications. Hence, there is a need for a

WSN platform that can scale up or down depending on structure size while keeping the sensors tightly synchronous. Satisfying both the scalability and the time synchronization requirements is a challenging problem. For example, global beacon synchronization can keep the time synchronized between sensors within a desired upper bound of a few microseconds, 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 bandwidth 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 scheduler [33]. To resolve this dilemma we propose a hierarchical architecture in which beacon synchronization 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 synchronization algorithm developed and implemented 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 synchronization limits. Section 4 describes the laboratory and field tests conducted on WISAN. Results are presented 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 compatible with IEEE 802.15.4 and can be utilized worldwide in the 2.4 GHz 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.

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(b)

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

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 pulseper-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 delivering them to a Coordinator. Each wireless sensor is equipped with the peripheral circuitry for interfacing of sensors and actuators. The peripheral circuitry 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 resolution. Signal conditioning is done using a fifthorder low-pass filter with cutoff frequency configurable over the wireless link. The noise resolution of the accelerometer in the frequency band of 100 Hz is 0.5 mg. 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 conditioning circuitry is also provided with a gain of 1000 to interface high precision strain sensors. The wireless sensors are enclosed using commercially available 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 efficiently 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, errorfree, 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 network also focused on developing application and transport layers of the network. Reliable data delivery and substantially reduced power consumption were achieved by a reservation-based scheduler implanted at the Coordinator level [33]. The network architecture of WISAN is a cluster tree. This choice is motivated by the fact that wireless sensors remain static on the structure and thus allow utilization of energy efficient hierarchical cluster architecture where most of the communications 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 associated 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 (30 m ) and a maximum of 16 clusters can be co-located in a given area enabling dense placement 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., wireless 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.

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Time Synchronization Algorithms

Multiple clusters of wireless sensors covering the monitored structure need to be closely

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Structural Health Monitoring 9(5) Application client: Control and data collection

TCP link

TCP link Long-range communications

TCP server

TCP server

WISAN sensors GPS

GPS

Serial connection

Coordinator 3

Coordinator 1

Coordinator 6

Coordinator 4 Coordinator 2

Coordinator 5

Short-range communications A cluster

Figure 2 WISAN network topology.

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-andcompare module of the microcontroller, corrects the internal timer by tSENSOR ¼ tTMR tBCN d (Figure 3) to compensate for accumulated time difference. The 8 MHz crystal oscillator used for the

sensors and the Coordinators has a frequency tolerance of 30 ppm. Hence, the maximum time difference between a sensor node and its Coordinator operating at 8 MHz would be 60 ms/s. Since in WISAN the beacons are emitted every 245.76 ms (beacon order ¼ 4) the maximal drift between a Coordinator and a sensor synchronizing on beacons is less than 15 ms (Tsensor max ). 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 provide 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 individual synchronization of every node in the network 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

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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 8 MHz clock or 0.125 ms clock interval. Emitted beacons are timed by a sequence of two timers:

8 MHz oscillator

0.125 µs

Timer B and MAC timer. Under conditions of perfectly stable crystal frequency the MAC timer counts 3125 times in 1 s (or 768 times between consecutive beacons). Given the maximum clock drift of 30 ms 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

Timer B (0–2559)

320 ms

Programmable divider Sampling trigger

tTMR Radio A beacon from Coordinator every 245.76 ms

tBCN

Capture module

ADC

Sensor

Correct timer B by SENSOR

= tTMR − tBCN

td

Figure 3 Time adjustments in the sensor node.

Radio Emit beacon 245.76 ms 8 MHz Oscillator

0.125 µs

Timer B (0–2559)

320 ms

tTMR GPS

TPPS = 1s tPPS

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Capture module

Dt = tTMR − tPPS Synchronize with PPS: •

Adjust Timer B by a every Bi ticks of MAC timer

•

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.

Figure 4 Time Adjustments in the Coordinator time synchronization algorithm.

MAC timer (0–767)


Bi ¼ Int 3125=Ai and Inth. . .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 Tpps ¼ 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 increments/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 interrupt the difference between the UTC reference and internal clock would be, iþ1 ¼ Diþ1 Di þ "i ¼ Diþ1 A0i . The value of A needs to be adjusted if "i 4 , 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 Coordinator 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 decremented 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 t0 = 0, A0 = 0, B0 = 0,D0 = 0, 0 = 0, Λ = 0, i = 0, a = 0.5 ms, N = 3125 2. At PPS interrupt i do a. Compute i = i +1 Di = tTMR – TPPS ti = Di – Di –1 + i –l §

b. if ti > Λ Ai = Int 〈Di /a〉 Bi = Int 〈N /Ai〉 Ai′ = Int 〈N /Bi〉 = Di –Ai′a − Ai = Int 〈N /(Bi + 1)〉, Ai = Int 〈N /(Bi – 1)〉 §

i

=

=

=

§

= Di –Ai a, = Λ = max { i , −i } i

−

§

−

i

= Di –Ai a

§ §

(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 synchronous, 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 120 ms time jumps due to time differences accumulated between consecutive PPS pulses. Such a large degree of asynchronicity is unacceptable for applications of modal identification. To avoid accumulation of the time errors the clock drift can be compensated by making a number of small adjustments distributed across the period TPPS. The upper bound error between consecutive MAC timer ticks can be minimized and kept within reasonable 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) þ R (t t0) ¼ (t0) þ Dt where t is the current time, (t0) is the time offset at the last measurement update t0, R is the frequency tolerance 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 partsper-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 estimates (t0) and R at regular intervals Tpps (1 s) and adjusts the clock to minimize (t) in the future. For any PPS interrupt i1 from the GPS receiver, all Coordinators set Timer B to zero ( i1 ¼ 0). The accumulated time difference on the next, i-th interrupt is given by i i1 ¼ R (Tpps) ¼ Di. Since Di is proportional to the crystal tolerance R it is safe to assume that under most realistic conditions value Di remains approximately constant over the period of Tpps. Then Di can be compensated over the next Tpps period in a fixed amount A of time adjustments (increments/decrements) of fixed size , thus keeping the maximum synchronization error significantly less than Di. The number of adjustments Ai and adjustment

period Bi are calculated as: Ai ¼ Int Di = , where

§

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else Ai = Ai –1 Bi = Bi –1 Ai = Ai –1

Figure 5 Parameter estimation in the Coordinator time synchronization algorithm.


is corrected by a reset to zero to avoid accumulation of the integer arithmetic error i0 ¼ i ðDi Di1 þ "i Þ. Since such a reset may cause an automatic increment of the MAC timer, a correction 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 j"i 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 accumulated between two Coordinators is 60 ms/s (480 timer B ticks). To find the bound " on this error, numerical computation of the error "i was performed for a range of accumulated differences between 0 and 120 ms. 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 identical time synchronized within an accuracy of 8 ms (TPAN max ). The time synchronization mechanism in WISAN relies on a simple hierarchical organization where synchronous operation of the global network is achieved by keeping wireless sensors tightly synchronized to a Coordinator and by

Total integer arithmetic error eA (ms)

8 7 6 5 4 3 2 1 0

–1

0

20

40

60

80

100

120

Delay value Di (ms)

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

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keeping all Coordinators tightly synchronized to GPS time references. Overall, the maximal synchronization error among sensors in WISAN sensor network (TWISAN ¼ TPAN max max þ Tmax ) is less than 23 ms. If, however, a crystal of 10 ppm tolerance is used in WISAN wireless nodes, the maximal synchronization error using the above algorithm would be less than 6 ms.

4

Experimental Analysis

4.1 Laboratory Tests Experiments were conducted to verify the performance of the proposed time synchronization algorithm. The first experiment tested synchronization 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 f1 ðtÞ ¼ sinð!0 t þ 1 Þ and f2 ðtÞ ¼ sinð!0 t þ 2 Þ. The respective Fourier transforms 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 ð1 2 Þ=!0 . The second experiment tested accuracy of time synchronization of WISAN network on the Coordinator level of hierarchy. A hierarchical network shown in Figure 2 was built with two separate 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 randomly 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 difference between the captured sinusoidal signals.

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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 reconstructed 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-synchronous and scalable architecture of WISAN to (a)

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 (19.8 m 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 (b)

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

Girder 1

Wireless sensors

Girder 2 Girder 3

Girder 4

Pan Coordinator station

Figure 8 Layout of the bridge test setup.


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 240.385 Hz and resolution of 14 bits. Traffic passing over the bridge was the source of bridge excitation. A number of experiments were conducted for data recordings of 30 s, 2 min, 5 min, and 10 min in duration. The cutoff of frequency of the programmable low-pass filter was varied between 40 Hz and 100 Hz to filter out high frequency 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 measured error for a set of five experiments varied between 2.6 and 10.4 ms with the mean of 5.1 ms and standard deviation of 3.1 ms. These results confirm the upper bound on time error within a cluster as 15 ms calculated in Section 3. The test of time synchronization between wireless sensors of a spatially distributed network synchronized by GPS time reference established an

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error range from a minimum of 0.1 ms to a maximum of 12 ms with the mean of 6.73 ms and standard deviation of 3.4 ms for a set of 5 experiments. These tests confirm the capability of WISAN sensors to continuously sample time synchronous data (with a precision of 23 ms) in network configurations that can be scaled to cover large structures. Results from the reconstruction of the aluminum 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 identification from vibration data. Results of the field experiments demonstrate the ability of WISAN to reconstruct mode shape from complex structures using the proposed scalable and time-synchronized architecture. We have identified several mode shapes at 9.155, 10.97, 26.23, 32.75, and 53.65 Hz (Figure 9). Vibration response of the bridge had a peak amplitude of 25 mg in the range of 8–12 Hz, 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.

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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 wireless 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 different 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 synchronicity of every wireless node. The results of laboratory tests show that the actual synchronization error is well within the bounds (23 ms) 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 kilometers. 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 commonly 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, hierarchical, 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 synchronization 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 proposed hierarchical network architecture can satisfy both scalability and time synchronization requirements. This scalable network architecture can be used to create time-synchronous sensor networks for applications 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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