de Battista - 2010 - Use of Imote2 with SHM-A wireless ...vibration.ex.ac.uk/publications/pdf/78_10176521.pdf · These wireless smart sensor nodes were also tried out during two field

5th World Conference on Structural Control and Monitoring 5WCSCM-10363

de Battista and Brownjohn 1

USE OF IMOTE2 WITH SHM-A WIRELESS SMART SENSOR NODES

N. de Battista Department of Civil and Structural Engineering, The University of Sheffield, U.K.

[email protected]

J.M.W. Brownjohn Department of Civil and Structural Engineering, The University of Sheffield, U.K.

[email protected]

Abstract

Over the past couple of decades there has been an increasing interest in applying wireless technology to testing and monitoring civil infrastructure. The most commonly cited reason for this is the extra expense, time and difficulty (and sometimes impossibility) involved in installing a wired network of sensors. Consequently, a number of proprietary and non-proprietary wireless sensor nodes have been developed or adapted for civil applications. This paper presents the use of Imote2 wireless sensor node platforms coupled with SHM-A sensor boards, both of which are available commercially. Using the ISHMP Toolsuite software package, a number of tests have been carried out in controlled environments in order to calibrate and verify the sensor boards, estimate their noise threshold and investigate the reliability of the Imote2’s radio communication capabilities. These wireless smart sensor nodes were also tried out during two field tests. Finally pilot studies were carried out to investigate the suitability of the Imote2 and SHM-A for measuring the accelerations of and estimate the inertia forces generated by the human body while performing various exercises.

Introduction The application of wireless technology to testing and monitoring of civil structures has been gaining considerable interest over the past decade or two. The main incentive for developments in this area is the difficulty and cost involved in installing a wired monitoring system on a structure. Sometimes it can even be impossible to run lengths of cables from the sensors to a data acquisition system. Although wireless technology has been around for a long time, its application to the field of civil engineering often presents particular requirements which need to be addressed: high sampling rates, data synchronisation, autonomous operation and low power consumption just to name a few. Several proprietary and non-proprietary wireless sensor nodes (WSNs) have been developed in order to monitor the performance or structural health of civil infrastructure. Comprehensive reviews are given by Lynch and Loh (2006) and Cho et al. (2008).

The basic components of a WSN are sensing capabilities, signal processing and analogue-to-digital conversion, wireless communication and a power source. In more recent years there has been the emergence of the term ‘Smart’ WSNs (or Wireless Smart Sensor Node – WSSN), referring to a node which, besides all the above, also has on-board computational capability and, often, memory storage. It is widely recognised that, especially in long-term monitoring exercises, it is not practical (and often unnecessary) to transmit entire time histories of the measured data. Instead, a popular approach is to process the measured data on-board the WSSN and transmit only the information that is required by the end user, such as FRF information or just a damage state indicator. Besides alleviating the communications burden on the network, this approach has the added advantage of usually consuming less power.

A popular approach to WSN hardware is to have a modular design where one or more of the functions are grouped on separate integrated circuit boards which can then be connected together. This offers the plug-and-play flexibility which might be needed when using the same hardware for different applications. This paper describes work done using SHM-A sensor boards connected to Imote2 WSSN platforms.


Imote2 and SHM-A

The Imote2 (IPR2400) is a WSSN platform designed by Intel and commercially sold by Crossbow Technology (Crossbow Technology, Inc., San Jose, CA). It incorporates a low-power PXA271 XScale processor and an IEEE 802.15.4 radio with an inbuilt 2.4GHz antenna. The Imote2s used in this study had external 2.4GHz antennas which provide better communication range. One of the major advantages of the Imote2 is that it has much larger memory than most other WSSNs. It combines 256kB SRAM, 32MB flash memory and 32MB SDRAM, allowing storage of algorithms and ample measured data. The Imote2 can be powered by batteries via a detachable battery board (IIB2400) or from energy harvesting devices (Miller and Spencer, 2010).

It is possible to implement various network topologies using the Imote2. The leaf nodes carry out the sensing and data processing. They transmit this data to the base station either directly or via a cluster head node if they form part of a sub-network (cluster) of nodes. The base station or gateway node issues commands to leaf nodes and cluster heads and receives data which it passes on to a PC via a USB connection. Although wireless data transmission between a leaf node and a gateway node is fast, collection of data from several nodes can be a long process since the data are transmitted from one node at a time. In addition, the process of writing the data to a text file on the PC is much slower than the actual wireless transmission itself.

Sensing capabilities are provided by detachable sensor boards which transfer data to the Imote2 platform via serial ports. The SHM-A sensor board (ISHMP, 2009b) has been developed by the Illinois Structural Health Monitoring Project (ISHMP) at the University of Illinois at Urbana-Champaign (UIUC) for use with the Imote2. The assembled WSSN is shown in figure 1. The SHM-A incorporates sensors to measure temperature and relative humidity, light and acceleration from a tri-axial MEMS LIS344ALH inertial sensor. It also has an analogue input port for an additional sensor. A 4-channel 16-bit QF4A512 ADC is used to digitise the analogue output from the accelerometer. This ADC also has in-built, programmable signal processing functions such as gain amplifiers and anti-aliasing filters.

The ISHMP Toolsuite (ISHMP, 2009a) is a free, open-source software developed at UIUC specifically for the SHM-A. It is a collection of utilities for network operation, applications for gathering sensing data and algorithms to process this data on-board, including system identification and damage detection algorithms. WSSNs often use the TinyOS operating system because it occupies little memory, it is power efficient and it is an open source software. Therefore it enables efficient use of the limited resources available on WSSNs. The processor on the Imote2 comes installed with the TinyOS as its default operating system and the ISHMP Toolsuite has been developed in the NesC programming language to operate on TinyOS. Despite its popularity, TinyOS imposes certain functional limitations. It does not support ‘multi-tasking’ and therefore it is not possible to carry out real-time monitoring: data first has to be collected and stored on board, then, once data collection has terminated, the processed or raw data can be sent back to the user. This imposes a limit on the

Figure 1. The WSSN (left) installed in a customised weatherproof enclosure (right)

battery board

external antenna connector

Imote2 WSSN platform

SHM-A sensor board


duration of any single monitoring period. In addition, TinyOS allows users little control over assigning priorities to tasks (Nagayama and Spencer, 2007).

Calibration and Verification of the SHM-A Sensor Board Acceleration data can be gathered using the RemoteSensing application in the ISHMP Toolsuite. In order to convert the output from the ADC on the SHM-A sensor board into measured units of acceleration, the data have to be shifted by a zero-g offset value and divided by a sensitivity factor (the calibration constants). The nominal offset and sensitivity values are quoted in the SHM-A documentation (ISHMP, 2009b) but these vary from one sensor to another. The variation can be as significant as ±0.1g and therefore it is important for the user to calibrate the sensor board before using it to collect data. The drift in the calibration constants with changes in temperature is catered for by automatic on-board correction using measurements from the temperature sensor on the SHM-A (Rice and Spencer, 2009).

Static calibration

Since SHM-A sensor boards can measure DC acceleration, in this study they were calibrated against static gravitational acceleration as explained in figure 2. Three WSSNs were first placed with the x-axis facing down so that they measured 1g in the x direction and 0g in the y and z directions. 30k samples were recorded from each accelerometer channel at a rate of 100Hz for a total record length of 5 minutes. The process was repeated with the WSSNs placed in each of the other two directions. This resulted in three data records (each 5 minutes long) for every axis of every WSSN: one measuring 1g and two measuring 0g (plus noise). By taking the mean of all the data points in each record, the noise was averaged out, giving a single output value equivalent to 1g (a1) and two output values equivalent to 0g (a0a and a0b) for each axis. The zero-g offset of each axis was taken as the mean of a0a and a0b while the scale was taken as the difference between a1 and the offset. The calibration constants obtained for the three WSSNs are shown in table 1.

Dynamic verification

Following the static procedure, the calibration constants were verified by comparing dynamic data obtained from the Imote2 WSSNs and from a wired reference sensor simultaneously. A Honeywell QA750 accelerometer was used as the reference sensor. The output from the QA750 was passed through a signal conditioning unit and read on a PC via a NI 9239 acquisition module connected to a NI USB-9162 interface. The NI 9239 has a 24-bit resolution and an in-built anti-aliasing filter. Data from the reference sensor were collected at 2000Hz and subsequently decimated down to 250 samples

Record 1

X = 1g Y = 0g Z = 0g

Record 2

X = 0g Y = 1g Z = 0g

Record 3

X = 0g Y = 0g Z = 1g

X-axis

a1

a0a

a0b

Y-axis a1

a0a

a0b

Z-axis a1

a0a

a0b

a1

a0 = (a0a + a0b)/2 SCALE = a1 – a0 OFFSET = a0

a1


a1


Figure 2. Schematic representation of the static calibration procedure for each node


per second. The signal measured by the WSSN was automatically passed through its in-built anti-aliasing filter with a cut-off frequency of 70Hz. Data were sampled on the Imote2s at 280Hz from the channel corresponding to the axis parallel to the reference sensor.

The reference sensor was mounted on a Perspex base which also served as a surface on which to attach the three Imote2 WSSNs. This arrangement is shown in figure 3. The sensors were positioned on an 11m long suspended slab strip, at about a third span. The first five natural frequencies of the slab are 4.4Hz, 17.0Hz, 26.1Hz, 29.1Hz and 37.6Hz (Racic, 2010). The slab was excited using a single APS-400 electro-dynamic shaker placed adjacent to the sensors as shown in figure 4. In order to induce a response having a significant amount of energy over a wide band of frequencies, a chirp type signal scanning from 1Hz to 30Hz in 90s was used as the excitation. A 5 minute record of response data was measured simultaneously using the WSSNs and the reference sensor for each of the three axes of the Imote2s. A 100s segment of each data record was then used to compare the measurements of the Imote2s and the reference sensor. Each of these segments contained at least a single frequency sweep. The comparison was done using an open-source MATLAB script written by the ISHMP at the University of Illinois at Urbana-Champaign (ISHMP, 2009c). Before the comparison was made, the wired and wireless data set pair was first synchronised (since the two sensors had different sampling rates). The maximum correlation factors of all the data set pairs are given in table 1. Figure 5 shows a typical time history comparison of one of the data set pairs. The small vertical shift between the wired and wireless data is likely to be due to a slight offset in the wired sensor calibration. The data from the QA750, which is a high-performance sensor, is much cleaner than that of the Imote2 which shows some higher frequency noise components especially during periods of low amplitude response. Figure 6 shows a comparison of power spectral densities and the coherence of the same data set pair. The transfer function of this data is shown in figure 7. From these figures it is evident that this data set pair was well correlated up to around 25Hz to 30Hz, which coincides with the highest frequency used to excite the slab. This was typical of all the data set pairs.

Table 1. Calibration constants obtained from the static calibration procedure and the maximum correlation coefficient from the dynamic verification. A correlation of 1 implies perfectly

correlated data while 0 implies completely uncorrelated data. Node ID 68 133 135 Axis X Y Z X Y Z X Y Z

0g-Offset [LSB] 14024 13977 14200 13850 14043 13990 13586 13600 13895Scale [LSB/g] 6741 6918 6822 6688 6801 6716 6672 6903 6815

Max. corr. coeff. 0.971 0.972 0.972 0.977 0.972 0.974 0.972 0.976 0.975

Figure 3. The QA750 reference sensor and Imote2 WSSNs mounted on a levelled

Perspex stand

Figure 4. The electro-dynamic shaker and sensors on the slab strip

Imote2 WSSNs

Imote2 battery packs

QA750 reference accelerometer

APS-400 electro-dynamic shaker


Figure 6. Power spectral density (top) and coherence (bottom) plots for data records from an Imote2 WSSN and the QA750 reference sensor. The red line on the coherence plot is drawn at

0.9 for reference.

Figure 5. Time histories for the Imote2 WSSN and the QA750 reference sensor – the complete 100s data record used for correlation (top) and a 3s clip (bottom)


Noise Threshold Although MEMS technology has progressed in leaps and bounds over the past years, a higher noise floor is one of the major disadvantages of MEMS accelerometers with respect to their larger counterparts. For example, it is quite typical for the noise threshold of a MEMS accelerometer to be two or three orders of magnitude larger than that of a high-end force-balance accelerometer such as the Honeywell QA750 which has a noise threshold of under 1μg (Honeywell, Redmond, WA). The noise threshold of an accelerometer can limit its use, especially when measuring very low amplitude vibrations (such as during ambient vibration testing), in which case the signal to noise ratio can become a critical factor. Very often, noise threshold and cost have an inverse relationship: the better the noise performance of an accelerometer the more expensive it is.

The data recorded from the Imote2s during the static calibration procedure described above was used to estimate their noise threshold. The data set consisted of three measurement records for every WSSN, each record being 5 minutes long. These measurements were done in a quiet (in terms of vibrations) basement laboratory late in the evening so as to minimise extraneous noise such as from vehicular traffic or from building occupants. The average peak RMS acceleration over all the three records was computed for each axis of every Imote2 WSSN. This was considered to be a good estimate of the noise threshold and the maximum and mean values for each axis are shown in table 2. It can be seen that the SHM-A sensor board has a higher noise threshold in the z-axis. The measured RMS noise levels are of the same order but higher than the average reported by Rice and Spencer (2009) for a 20Hz bandwidth and over 400 times larger than that of the Honeywell QA750 accelerometer.

Axis Maximum of all three WSSNs [mg] Mean over all three WSSNs [mg] X 0.4991 0.4733 Y 0.6405 0.5248 Z 1.5198 1.3429

Figure 7. Transfer function between data records from an Imote2 WSSN and the QA750 reference sensor

Table 2. R.M.S. noise estimated for each axis


The latest sensor board to be developed by the ISHMP at the University of Illinois at Urbana-Champaign for the Imote2 platform is the SHM-H (Jo et al., 2010). This sensor board incorporates a (more expensive) accelerometer with a reported noise threshold of 0.06mg over a 20Hz bandwidth, providing a significant improvement over the SHM-A.

Reliability of Radio Communication The ability to transmit data wirelessly is essentially what differentiates a WSN from a wired sensor. Therefore it is important to judge the radio communication capabilities of WSN hardware. The ISHMP Toolsuite makes use of the Reliable Communications Protocol (Nagayama and Spencer, 2007) to improve the reliability of data transfer between nodes. A sending node repeatedly transmits the data until it receives a confirmation of receipt from the receiving node or nodes, or until a pre-defined time-out period expiries. Since this involves repeated data package transmissions, it slows down communications but it improves reliability in non-ideal environments.

The Imote2s were tested on two structures which were under construction (figure 9). The first structure was a three storey open plan building. The construction consisted of a steel frame supporting composite steel plate and concrete slabs. The second structure was a pedestrian footbridge. It consisted of a reinforced concrete deck supported by a dense mesh of steel circular hollow sections winding around the deck. Both structures presented far from ideal wireless communication environments due to the large amount of steel present, but they were typical of structures which one might want to monitor. In both cases, attempts were made to measure acceleration of the concrete surface, with the Imote2s placed on a Perspex plate sitting directly on the concrete (figure 8). This presented another obstacle in the form of radio waves reflecting off the concrete and possibly interfering with the waves reaching the gateway node, a typical problem with “omni-directional” antennae. In both structures, it was difficult to communicate with leaf nodes and retrieve measured data from them. During the vast majority of attempts, one or more of the nodes either did not receive the command to start measuring data or failed to transmit the data back to the gateway node.

Figure 8. One of the Imote2 WSSNs and QA750 accelerometers used to acquire acceleration data during the field tests

Figure 9. The structures used to test the Imote2 WSSNs: an open plan building (left) and a footbridge (right)


In order to improve the communication capabilities of the Imote2s, co-axial extension cables were made in order to be able to position the antenna away from the WSSNs. Lengths of 1m, 2m and 3m RG-58 cables were used with SMA connectors at both ends. A simple test was carried out in an indoor corridor so as to ensure that the extension cables did not attenuate the signal excessively. Using a single leaf node first without an extension cable and then with each of the three lengths of extension cable, the TestRadio application in the ISHMP Toolsuite was used to send 1000 dummy data packets from the gateway to the leaf node and back to the gateway. This was done for separation distances between the gateway and leaf nodes ranging from 5m to 60m. In all cases, all the data packets were transmitted over the two legs with no loss. Although this test did not give any indication of signal strength, it verified that extension cables up to at least 3m long could be used in order to position the antenna in a better communication environment without undermining the success of data transfer significantly. The improvement that could be achieved in the communication environment by using these extension cables outweighs the attenuation of the signal.

Human Motion Acceleration The Vibration Engineering Section within the Department of Civil and Structural Engineering at the University of Sheffield is conducting ongoing research on the dynamic forces imposed by humans on structures (Racic et al., 2010). The aim is partly to study the correlation between the motion of the human body during various activities and the forces imposed on a supporting structure. The forces are measured either by an instrumented treadmill (for walking and running) or by a force plate (for bouncing and jumping). So far, accelerations of the human body have been measured using a Codamotion sensor system (Charnwood Dynamics, Leicestershire, UK) which actively tracks markers attached to a test subject.

Although it has proven to be useful for laboratory experiments, this system is limited to use in unobstructed indoor environments. Therefore an acceleration measurement system which can be used in the open field is being sought. One obvious solution would be to use WSNs. Trial tests were conducted to investigate the suitability of the Imote2s for this purpose. One WSSN was attached to the lower back of a test subject. A Codamotion marker was also fixed to the enclosure of the WSSN to provide a reference measurement (figure 10). Acceleration data was recorded in the vertical axis from the Imote2 WSSN and the Codamotion system simultaneously. This was done for walking, running, bouncing and jumping (figure 11). Data was sampled at 100Hz on both the Imote2 and the Codamotion system.

One of the limitations of the Imote2 is that the user has no direct control over the exact start of the Imote2’s data collection. When the command to start sensing is given, the nodes first synchronise their clocks, then they wait a pre-defined period of time before starting to collect data without any additional user interaction. Therefore at the beginning of each measurement the test subject was asked to do a single sharp vertical movement in order to create a spike in the time histories of both the Codamotion system and Imote2. This was then used to match the two data records in the time domain. Both data sets were passed through a Butterworth 4th order low-pass digital filter with a cut-off frequency of 20Hz, which is generally the highest frequency of interest in human motion. The DC component present in the Imote2 data, which arises from the fact that the sensor is not aligned in a perfectly vertical axis, was removed by de-trending the time history.

Table 3 summarises the correlation coefficients between the time histories from the two measurement systems. This coefficient is a measure

Imote2 WSSN in enclosure with

external antenna

CODA system marker

Figure 10. The Imote2 WSSN and CODA system marker attached to the lower back

of the test subject


of the match between the ‘shapes’ of the two time histories but it is not directly related to the actual values at each time instant. A correlation coefficient of 1 implies that the two time histories follow each other perfectly but it does not necessarily mean that they contain the same values. It can be seen that, for all four activities, the data from the Codamotion system and the Imote2 correlate very well. This confirms that the two measurement systems are well synchronised, as is also evident from the time history extracts in figure 12. During running and jumping, where accelerations in excess of 2g were recorded by the Codamotion system, the Imote2 was unable to measure the peak accelerations since the range of the SHM-A sensor board is ±2g (ISHMP, 2009b). In addition, the accelerations measured by the two systems did not match perfectly since, while the Codamotion system records data in the global axes, the Imote2 can only record data in its own local axes. Therefore the angle at which the Imote2 is attached to the test subject (which is impossible to control accurately) introduces an error in the data. It would be ideal if the WSSN had a gyroscope included on its sensor board to enable the measurements to be converted into the global axes. Figure 13 shows the frequency content of the data obtained from the two measurement systems.

The degree to which the data obtained from the Imote2 and the Codamotion system match each other in both the time and frequency domains is very encouraging. Future studies will look further into combining acceleration measurements obtained from WSSNs in all three global axes with force data measured from the treadmill and force plate in order to improve our understanding of human dynamic forces.

Table 3. Correlation coefficients for the time histories of the Codamotion sensor system and

the Imote2 WSSN for all four activities

Activity Correlation coefficients

Walking 0.900 Running 0.934 Bouncing 0.987 Jumping 0.978

Figure 11. Acceleration data being recorded simultaneously by the Imote2 WSSN and the CODA sensor system for walking (left) and jumping (right) activities


Figure 12. Segments of the time histories measured by the CODA sensor system

and the Imote2 WSSNs for walking, running, bouncing and jumping activities


Figure 13. Frequency content of the signals measured by the CODA sensor system and the Imote2 WSSNs for walking, running, bouncing and jumping activities


Conclusion In this paper, work done using three WSSNs comprising Imote2 wireless sensor node platforms and SHM-A sensor boards has been presented. A brief overview of the hardware setup and the ISHMP Toolsuite software was given. The WSSNs were first calibrated using a static method. The calibration constants and the measurements from the calibrated WSSNs were verified in the time and frequency domains by comparing them with data from a wired reference sensor. The noise threshold of the three SHM-A sensor boards was estimated and this resulted to be slightly higher than that reported by the developers of the sensor boards. A simple test based on transmitting dummy packets of data was used to investigate the reliability of the radio communications between leaf and gateway nodes at various separation distances in an ideal environment. No data packets were lost at up to 60m separation even when using antenna extension cables up to 3m long. However when used during field tests on structures under construction, data communication proved to be much less reliable. Finally the possibility of using the Imote2s to measure the acceleration of the human body while walking, running, bouncing and jumping was investigated through a number of pilot tests. The results were encouraging and this application will be the subject of future work.

An online discussion forum has been set up for Imote2 users. It is hosted by the Vibration Engineering Section at the University of Sheffield and it can be accessed at http://vibration.shef.ac.uk/imote2_forum/. The forum serves as a platform where knowledge sharing and discussion about various issues related to the Imote2 can take place.

Acknowledgements

The authors would like to acknowledge the contribution of Dr Vitomir Racic in the tests involving the measurement of human motion acceleration.

References

Cho, S., Yun, C.B., Lynch, J.P., Zimmerman, A.T., Spencer Jr., B.F., and Nagayama, T. (2008), “Smart Wireless Sensor

Technology for Structural Health Monitoring of Civil Structures," Steel Structures, 8(4), 267-275. ISHMP (2009a), “ISHMP Getting Started Guide,” University of Illinois at Urbana-Champaign, Urbana, IL,

http://shm.cs.uiuc.edu/files/GettingStarted_Preconfigured.pdf ISHMP (2009b), “SHM-A Board Multimetric Imote2 Sensor Board, Datasheet and User’s Guide,” University of Illinois at

Urbana-Champaign, Urbana, IL, http://shm.cs.uiuc.edu/files/SHM-A_Datasheet.pdf ISHMP (2009c), “Calibration Guide for Wireless Sensors,” University of Illinois at Urbana-Champaign, Urbana, IL,

http://shm.cs.uiuc.edu/files/CalibrationGuide.pdf Jo, H., Rice, J.A., Spencer Jr., B.F., Nagayama, T. (2010), “Development of a High-sensitivity Accelerometer Board for

Structural Health Monitoring,” Proc. SPIE 7647, 764706, 8-11 March 2010, San Diego, CA Lynch, J.P. and Loh, K.J. (2006), “A summary review of wireless sensors and sensor networks for structural health

monitoring,” Shock and Vibration Digest, 38(2), 91-128. Miller,T.I. and Spencer Jr., B.F. (2010), “Solar Energy Harvesting and Software Enhancements for Autonomous Wireless

Smart Sensor Networks,” NSEL Report Series Report No. 22, University of Illinois at Urbana-Champaign, Urbana, IL, (awaiting publication).

Nagayama, T. and Spencer Jr., B.F. (2007), “Structural Health Monitoring Using Smart Sensors,” NSEL Report Series Report No. 1, University of Illinois at Urbana-Champaign, Urbana, IL.

Racic, V., Brownjohn, J.M.W. and Pavic, A. (2010), “Reproduction and application of human bouncing and jumping forces from visual marker data,” Journal of Sound and Vibration, 329, 3397-3416.

Rice, J. and Spencer Jr., B.F. (2009), “Flexible Smart Sensor Framework for Autonomous Full-scale Structural Health Monitoring,” NSEL Report Series Report No. 18, University of Illinois at Urbana-Champaign, Urbana, IL.

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de Battista - 2010 - Use of Imote2 with SHM-A wireless ...vibration.ex.ac.uk/publications/pdf/78_10176521.pdf · These wireless smart sensor nodes were also tried out during two field