WSNs are spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, and pressure, and to cooperatively pass their data through the network to other locations. LORD Sensing develops and manufactures wireless sensor data acquisition systems, inertial sensors, micro-displacement sensors, and software for a wide range of custom and embedded applications.
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Wireless Sensing Networks
Accionado por:. Search Windows Server How to keep VM sprawl in check Virtualization improves hardware use, but the pendulum can swing the other way and result in an overallocation of resources. The natural frequencies were extracted, and the cable force was determined by employing a non-linear force-frequency relationship that was obtained by numerical simulations.
Further vibration measurements were conducted at several other stay cables under ambient excitation.
Thereby, the external sensor MPU was connected to the RPi, and the smartphone was used as a central control unit. This setup was chosen because it features both sufficiently high resolution and a sampling rate necessary for the validation under ambient vibrations.
The measurements showed that at least 10 natural frequencies could be identified in each of the measurements for the investigated stay cables. Again, the cable forces were identified applying the non-linear force-frequency relationship, and a good agreement with the reference cable forces was found. The predicted cable forces were within the limit of a 1. To sum up, the RPi-based system and a specific smartphone Sony Xperia Z5 were able to identify frequencies with sufficient accuracy even under ambient wind excitations.
The mobile measurement system was further employed in a different usage scenario at a highway bridge located in Central Germany. The considered box girder bridge has unique site conditions being partly founded in a quarry see Figure 11 where every few months explosive excavations are performed. The monitoring task was to determine the maximum vibration velocity at the top of the tallest bridge pier due to ground shaking induced by these blasts.
An allowable peak velocity had been previously determined from detailed studies of the structural performance under typical base excitation, thus serving as a threshold value to judge structural integrity on the basis of on-site measurements. The acceleration at the top of the tallest bridge pier was measured in three axes by an external MPU sensor connected to the RPi-based measurements system, two smartphones Sony Xperia Z5, Nexus 4 , and a tablet Nexus 7 as well as a high-quality reference system. The recorded acceleration time histories were then integrated over time to compute the velocity time history and to determine the absolute peak velocity.
Thereby, signal processing such as filtering and detrending of the individual signals was necessary.
The peak velocities determined by the different systems are compared in Table 4. In comparison to a high-quality reference sensor system, the results obtained by the alternative mobile measurement systems are generally in good agreement. The superiority of the RPi linked to the MPU accelerometer over the smartphones in terms of sampling rate can be seen in the smallest deviations which are below 1. Comparison of the different measurement systems in terms of their sensing characteristics and determined peak velocities along with the relative difference to the high-quality reference system.
It should be noted that, shortly before the blast, the bridge had to be closed for traffic. The reopening of the bridge required the explicit approval of an engineer whose decision is based on the non-exceedance of the peak velocity threshold. In order to keep the interruption of the traffic on the highway as short as possible, it is crucial to ensure reliable, robust, and fast data processing. Since all the necessary data processing steps are implemented in the smartphone app, measurement and data analysis is integrated in a single mobile device, and the obtained results show satisfactory accuracy for judging the acceptability of the measured vibrations.
The system employing a smartphone as a control unit for the RPi-based measurement system with MPU is a powerful solution for the engineer in charge. Knowing that the obtained peak velocities are typically well below the threshold value, it is also possible to employ the smartphone Nexus 4 as a standalone solution by defining the limit of accuracy. Since there is a trend of increasing sampling rates with which smartphones can record accelerations, it is expected that results of higher accuracy will be found with future phones, making smartphones an even more attractive choice.
Since the smartphone is highly mobile and the smartphone application can trigger and control measurements, a second smartphone was attached at the bottom of the pier without much effort to collect additional information about the structural behavior. The presented measurement system also allows simultaneous measurement at all bridge piers while the RPi-based wireless sensor network is employed.
RPi nodes can also be used as a WiFi-extender in the case of long distances, and even smartphones can easily be integrated into the network. The determination of natural frequencies and integral values such as velocities and displacements can be obtained from a single sensor attached to the structure at a suitable position. The benefit of having a mesh-based wireless sensor network providing synchronized sensor nodes is that dynamic properties such as mode shapes and the corresponding structural damping can also be identified.
This advantage will be demonstrated in the following sample application. This application considers a simply supported steel beam as shown in Figure The goal was to determine the frequency, the shape, and the damping of the first five vertical bending modes as in typical SHM tasks. Experimental setup with sensors attached at the bottom of the beam. Wooden pieces are used as support for the sensor nodes. Two different measurement systems are available for the measurement campaign: first, a high-quality reference system including highly sensitive piezoelectric sensors PCB A03 connected to a data acquisition unit featuring a synchronous input channel and 24 bit signal conversion; second, the alternative RPi-based measurement system using external MPU sensors [ 31 ].
In total, three piezoelectric sensors and seven sensor nodes were employed in the vibration test. The sensor setup is shown in Figure 12 and Figure The seven wireless sensor nodes are placed equally throughout the length of the beam with a center-to-center distance of 0. The sensing characteristics and the costs of the two applied measurement systems are summarized in Table 5. The superiority of the reference system can be seen from the much higher resolution.
In addition to the synchronous data acquisition, it features an integrated anti-aliasing filter and allows for sampling rates up 40 kHz. However, the sampling rate was set to Hz for this experiment. Properties of the utilized measurement systems for the steel beam experiment [ 31 ]. Both piezoelectric and MEMS accelerometers were mounted to steel plates that were attached to the beam by means of two magnets, as shown in Figure The RPi sensor nodes were located next to the beam on wooden supports. After powering the RPi, the wireless network and the meshing was set up automatically.
Prior to the measurements, the time synchronization of the sensor nodes was conducted via the previously presented frontend software, which was further used to control the measurements. View of the sensor mounting plate equipped with a high-quality piezoelectric sensor and an external accelerometer attached to the RPi. The accelerations were recorded for more than s in order to achieve a frequency resolution higher than 0.
The time histories were measured simultaneously by all sensors of both systems. For further processing, the acceleration time histories of the MEMS accelerometer were resampled to a frequency of Hz. System identification was performed using the Matlab toolbox MACEC [ 32 ], which employs the covariance-driven approach of stochastic subspace identification and thus determines the modal information of the beam from the recorded sensor data. Suitable modes were then chosen from the stabilization diagram. The natural frequencies and damping ratios obtained from the two applied systems are compared in Table 6.
The natural bending frequencies have been identified and show good agreement between the two systems with a maximum difference of 0. Regarding the determined damping, a greater deviation that is more dominant for the lowest modes can be seen. It should be noted that the considered steel beam has a very low inherent damping, making it difficult to measure accurately. The mode shapes obtained for the first five modes are shown in Figure 15 alongside those computed analytically. Note that the analytical mode shapes are only an approximation of the real behavior, as they disregard sensor masses as well as potential imperfections in the system due to variations of weight and stiffness.
Both systems show good agreement especially in the first three modes. The general shape of the fourth and fifth mode are still determined well from RPi measurements; however, the amplitude at the sensor locations shows deviations from the analytical shape. The reference measurement system shows agreement with the analytical shape that is broadly similar to that of the RPi system.
Still, the fourth mode shows a significantly better performance, as indicated by the modal assurance criterion MAC given in Table 6.
Why wireless sensor networks for datacenters are becoming a must
The MAC value for the highest modes is still greater than 0. However, the reference mode shapes lack supporting points to properly reflect the higher sine waves due to the smaller number of sensors used. It is worth considering this aspect in the context of the cost of the measurement system. The results clearly show that the RPi system obtains modal information, specifically mode shapes, not available from a reference system of at least 20 times the cost.
Of course there are SHM applications where such a highly accurate system may be strictly required. Furthermore, it is possible to overcome the above-highlighted shortcomings of a limited sensor count by performing multiple measurements at different sensor arrangements. Yet, where the presented WSN solution is sufficiently accurate for the task at hand, it represents an attractive alternative to traditional technologies, including proprietary WSN systems, at a fraction of their cost.
This paper has presented a combined hardware and software solution capable of performing highly accurate measurements in a WiFi-based meshed configuration of sensing nodes. The main focus was the coherent software framework developed for off-the-shelf and cost-effective microcomputer hardware such as RPi, turning them into sensing nodes that also perform network communication. A smartphone app that facilitates the initiation and management of the measurement process and allows for data processing and storage is presented. Further, the current implementation, by polling their internal sensors, allows for measurement with smartphone devices integrated into the mesh.
Several test measurements that focus first on the specific data acquisition characteristics of the systems in a meshed configuration are reported.
What is Wireless Sensor Networks?
The results confirm the high performance of the measurement system in terms of stable sampling at high sampling rates up to 1 kHz and an accurate time synchronization between nodes, with time shifts being reliably under 0. A second set of tests showed that the systems can perform well in typical SHM tasks such as the frequency measurement and force computation of stay cables, velocity measurement based on acceleration sensing, and modal identification of structures. In addition to the quality of measurements, workflows are relatively simple and user-friendly due to the highly integrated nature of hardware and software components.
The RPi-based monitoring system presented is a flexible, highly mobile, cost-effective, and robust system that has been found to offer measurement characteristics in a meshed configuration that is sufficient for a wide range of SHM applications and that are superior to many standard WSN systems. Finally, the solution presented here is not based on any proprietary technology and lends itself well to teaching purposes. Conceptualization: G. National Center for Biotechnology Information , U.
Journal List Sensors Basel v. Sensors Basel. Published online May 3. Author information Article notes Copyright and License information Disclaimer. Received Mar 31; Accepted Apr This article has been cited by other articles in PMC. Abstract Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. Keywords: Raspberry Pi, smartphones, wireless sensor networks, vibration measurements.
Introduction The mechanical properties of structures change over the course of their lifetime. Hardware and Measurement System 2. Concept Since conventional measurement systems in the context of SHM are costly and often bound to specific hardware, the main goal of the present work is to provide two alternatives based on consumer grade and mobile hardware. Open in a separate window.
Tyndall National Institute - WSN (Wireless Sensor Networks)
Figure 1. Figure 2. Table 1 Summary of implemented sensor driver modules. Smartphone Platform An Android application was developed by the authors in a previous work to assist the process of smartphone-based measurements [ 6 ]. Figure 3. Mesh Network A mesh network is used to build a flexible mobile ad hoc sensor network on site, which enables the user to reach every node in the mesh network by connecting to a dedicated node of choice. Figure 4. Exemplary network topology consisting of four RPi nodes and four smartphones. Interfaces of Measurement System An RPC server component is utilized to listen for commands to configure attached sensor hardware, to discover nodes in the mesh network and to control actual measurements.
Figure 5. System Performance 3. Sampling Regularity and Time Synchronization A test was conducted to assess the quality of the presented measurement system. Figure 6. Figure 7. Figure 8. Figure 9. Figure Limitations of the System The main limitations of a smartphone as a measurement node are the on-board components such as accelerometer, processor and storage space, all of which cannot normally be exchanged.
Applications The technology platform as it is presented in this paper has been used for several measurement campaigns including the following monitoring tasks: the identification of natural frequencies of cantilevered balcony plates using a meshed sensor network controlled by the smartphone application, the identification of vertical and rotational natural frequencies of a dynamic wind tunnel rig using RPi and external sensors, the identification of natural frequencies of a pole structure for model updating, the determination of prestressing forces of external tendons in a highway bridge [ 15 ], the identification of stay cable forces at Queensferry Crossing bridge, the determination of peak velocities from acceleration measurements at a bridge pier during blast operation, and the identification of modal properties of a simply supported steel beam.
Estimation of Stay Cable Forces at Queensferry Crossing During the construction of Queensferry Crossing QFC , a cable-stayed bridge over the Firth of Forth near Edinburgh, vibration measurements were taken on stay cables to determine their tension forces. Determination of Peak Velocities during Blast Operation at Schindgraben Bridge The mobile measurement system was further employed in a different usage scenario at a highway bridge located in Central Germany.
Table 4 Comparison of the different measurement systems in terms of their sensing characteristics and determined peak velocities along with the relative difference to the high-quality reference system. Identification of Modal Information of a Steel Beam This application considers a simply supported steel beam as shown in Figure Table 5 Properties of the utilized measurement systems for the steel beam experiment [ 31 ]. Conclusions This paper has presented a combined hardware and software solution capable of performing highly accurate measurements in a WiFi-based meshed configuration of sensing nodes.
Author Contributions Conceptualization: G. Funding This research received no external funding. Conflicts of Interest The authors declare no conflict of interest. References 1. Swartz R. Kim S. Lynch J. A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib. Holovatyy A. Development of a system for monitoring vibration accelerations based on the Raspberry Pi microcomputer and the ADXL accelerometer.
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