a wireless distributed sensor network with low-cost...

A wireless distributed sensor network with low-cost vibration sensors for structural health monitoring

J.J.M. van de Sande1, T.G.H. Basten1, H.C. Hakkesteegt1 1 TNO Technical Sciences Oude Waalsdorperweg 63, 2597 AK The Hague, The Netherlands e-mail: [email protected]

Abstract The amount of infrastructure assets such as bridges, process installations and off-shore wind turbines is increasing, accompanied by a rising need for advanced maintenance strategies to reduce inspection and maintenance costs. These maintenance strategies require reliable maintenance status information. Vibration sensing can supply added value for structural integrity monitoring. Large structures, however, require the use of a large amount of sensors which increases the amount of collected data. This puts its demands on data processing and cabling in wired applications with centralized processing. In this paper, a wireless solution is described by means of outlining the development of a distributed sensor network using low-cost wireless vibration sensors. The current network is designed to perform operational modal analysis by applying a relatively simple peak picking algorithm. In this way the eigenfrequencies and mode shapes of a structure can be monitored, which give input to methods for damage detection and characterization. As wireless sensors have a limited communication bandwidth and concurrent data transmissions will flood radio traffic, decentralized data processing is performed to reduce the required amount of communication. Attention will be given to the processing architecture and data flow as well as methods applied to reduce energy consumption. Preliminary results of tests on a scale model of a wind turbine will be presented to assess the quality of the approach. Further, a connection is made with an analysis and optimization tool called Dynamic Adaptive multi sensor networks Architectures (DynAA). With this tool, among others, an operation lifetime analysis can be made with respect to energy-limited, distributed sensor networks. Also, the tool is utilized for optimizing communication parameters in order to extend the maximum operation time of the network.

1 Introduction

Monitoring the structural integrity of infrastructure assets receives increasing interest. The lifetime of many civil structures which are in use today has reached the design lifetime. This asks for increased monitoring effort for the remaining lifetime. Also for offshore structures, where the costs of operational maintenance are very high, alternative maintenance strategies such as condition based maintenance will directly result in substantial cost reductions. For such strategies, monitoring the current condition is critical. These maintenance strategies require reliable information about the maintenance status. Vibration measurements can give information about the structural integrity. However, large structures require the use of a large amount of sensors to have proper insight in the global dynamic behavior. This puts its demands on data processing and cabling in wired applications with centralized processing.

In this paper, a wireless solution is described for large scale vibration monitoring by means of outlining the development of a distributed sensor network using low-cost wireless vibration sensors. First, in section 2 the distributed peak picking approach is described which is a very straightforward and efficient method for determination of the modal parameters of a structure. In section 3 the implementation on a hardware platform is described. Section 4 describes an example and experiments which are performed using a scale model of a wind turbine together with a discussion of the first results. Section 5 describes the development towards a large scale application, while section 6 describes a design and analysis tool for large scale,

437

distributed, wireless networks that can assist in architecture design and evaluation. Chapter 7 ends with discussion and conclusions.

2 Vibration monitoring

The first step for many vibration based methods for structural health monitoring is to determine the modal parameters of the system. The second step is to process this data and to determine whether there is damage or not, to find the location of potential damage and its severity. The last step is to estimate the remaining lifetime of the structure. Various algorithms are available for operational modal analysis, but not all of them are suitable to be implemented in a wireless sensor network due to their high memory and processing requirements. In [1] several methods for operational methods are put into perspective. Based on this overview several methods and the consequence for application in a wireless network were discussed in [2]. The most classical frequency-domain approach for operational modal analysis is the Peak Picking (PP) technique. In this case the modal frequencies are directly obtained from the peaks of the Auto Spectral Density plot. Then, the mode shapes are extracted from the column of the spectral matrix which corresponds to the same frequency. Although the method is very straightforward and efficient, this method has certain disadvantages. The main disadvantage is the inaccuracy in the case of closely spaced modes. The Frequency Domain Decomposition (FDD) is an extension of PP which aims to overcome this disadvantage. The PP and FDD methods are frequency domain methods. More advanced methods are time domain methods, such as the Stochastic Subspace Identification (SSI) method. Although these methods give generally very accurate results, these methods are computationally very intensive and require a lot of data interchange, which is undesirable in a wireless network.

As a first step in the current study the Peak Picking method was implemented on a wireless platform. This method is very easy to decentralize in a wireless network and needs limited computational power and communication between the wireless sensors. The wireless node and the implementation will be discussed in the next section.

3 Implementation in a wireless network

The transition from theory to practice is known to come with its issues, constraints and limitations. In this case the transition from a central application of the Peak Picking method is considered, where initially wired nodes would collect an almost continuous flow of data. A PC with more or less infinite processing power and unlimited external power supply would be able to process all the data. In essence, the main challenges predominantly reside from the step towards an operational solution with low maintenance and deployment costs. In order to reduce deployment and hardware costs and to enable scalable solutions with hundreds to even thousands of sensors, one has to get rid of wires. The consequences of this are the need for wireless communication; a local power supply and at the same time low-cost hardware. Consecutively this leads to the need for low-power hardware; distributed processing for reducing the amount of communication and ways to cope with limited memory and processing power. This chapter describes the steps to a solution that tries to cater for these requirements, with respect to both hardware and processing. The initial architecture for a small sensor network performing distributed operational modal analysis, which is partly based on the work by Zimmerman et al. [11] will be outlined.

3.1 Hardware platform

The nodes used for performing Vibration Monitoring (VM), among others comprise an MSP430-microcontroller, 8 KB of internal RAM memory and some external memory (Flash and SRAM), a packet-based 868 MHz radio chip, a USB-interface and user I/O (buttons and LEDs). On top of the node a sensor board is attached containing a digital 3-axis accelerometer from Analog Devices (ADXL345). The

438 PROCEEDINGS OF ISMA2014 INCLUDING USD2014

accelerometer has a 13-bit resolution and a selectable dynamic range up to +/- 16g. The sensor is accessed over an SPI interface. The output bandwidth can be selected up to 3200 Hz.

The firmware for supporting the measurements is developed using TinyOS [9]. TinyOS, written in nesC, is an open source operating system designed for low-power wireless devices. In practice, TinyOS provides a collection of drivers for microcontrollers and integrated circuits such as the CC2420 [8] and CC1101 [6] radio chips. These drivers, called components, each consist of a configuration and an implementation module. The configuration defines the utilization and wiring of other components, while the module provides the executable code that makes use of the component interfaces. A user can write his own application by creating its own component(s), utilizing the already existing components in TinyOS. Hence, TinyOS provides an abstraction layer to simplify application development. The low-level driver software in general is written in C, while providing abstraction construction to support the TinyOS language nesC.

3.2 Implementation of the distributed peak picking algorithm

Zimmerman et al [11] proposed an architecture for a distributed version of the peak picking method. Their approach forms the basis of the current implementation. Figure 1 depicts a block diagram of the tasks performed on the N sampling nodes and the Central Station (CS), which comprises a PC and a node that are connected through USB.

Figure 1: Block diagram of the processing scheme on a sampling node and the Central Station (Node + PC). Each sampling node performs local data collection and processing. The CS and the sampling nodes exchange information with which the final eigenfrequencies and mode shapes are determined on the CS.

On reception of a trigger signal from the Central Station (CS), each node performs an accelerometer measurement by means of collecting a number of accelerometer samples of a single axis at a certain sampling rate. The node performs a Fast Fourier Transform (FFT) on the samples and determines the absolute values of the FFT output. After this, peak picking is performed on the resulting absolute values of the frequency spectrum, after which the center frequencies of the 10 highest peaks are transmitted to the CS. The CS collects this information from all the sensor nodes and determines the eigenfrequencies of the system. The frequency indices of these eigenfrequencies are transmitted to the sensor nodes that, in their turn, determine the mode shape information of the concerning frequency indices and transmit it back to the CS. The mode shape information consists of the imaginary part of the spectrum at the indices of the eigenfrequencies received by the CS. The CS collects all the mode shape information and determines the shape of the structure during the measurement for each eigenfrequency, by coupling the mode shape

CONDITION MONITORING AND DAMAGE DETECTION 439

information to the node locations. The eigenfrequencies are defined to be the frequencies that occur the most among all nodes.

3.3 Parameter derivation

Having the global overview of data and control flow, there are still some parameters remaining undefined. The sampling frequency of the accelerometer and the amount of samples collected by the accelerometer are of influence on the quality of the results as well as the feasibility of an implementation on an embedded platform with respect to a limited amount of memory, respectively. To this end, also the quality of the utilized, low-cost hardware needs to be taken into account with respect to sampling rate accuracy and accelerometer sensitivity.

Considering VM, we are interested in vibration information in a frequency range of 0 to 200 Hz, the range in which the interesting eigenfrequencies of many large scale structures reside. Obeying Nyquist’s law, this leads to a desired sampling frequency of approximately 400 Hz. Further, because of the fact that eigenfrequencies of structures can be close together, the frequency resolution is required to be 0.75 Hz. The use of low-cost components comes with its compromises however. The utilized accelerometer resides on a chip having its own clock, leading to inter-node accelerometer deviations.

3.3.1 Accelerometer clock inaccuracy

The clock of the accelerometer is highly inaccurate and differs between different accelerometers. When presetting a specific sampling frequency, the actual achieved sampling frequency can differ up to 10% between the different nodes. One of the features of the accelerometer, though, is to measure the clock time after the collection of 75% of the samples. In this way, the actual sampling frequency can be determined. This value can be taken into account when determining the center bin frequencies of the eigenfrequencies. Still, though the center bin frequencies are corrected, they are not exactly the same between the nodes, as each node results with a different sampling frequency but the same amount of samples. This means a phase difference will result between the nodes due to a bin frequency mismatch, when applying the same (in-phase) reference signal. To reduce this effect, the time signal should be resampled to obtain the same final sampling frequency at each node.

Measurements have been performed on a vibrating platform in order to excite the accelerometers of two nodes with the same in-phase reference signal. Figure 2 shows the situation with and without time-domain pre-resampling before applying an FFT, of two nodes measuring the same input sine wave of 50 Hz on the same physical location. This should lead to a phase difference of 0 radians at the excited frequency.

Figure 2: Phase information of the frequency spectra of the measured accelerometer signal of two nodes positioned on a vibrating platform. The nodes vibrate in-phase. Left is the situation when no resampling is performed in the time domain signal before performing the FFT. Right is the situation when time-domain resampling is performed before computation of the FFT. Though the center frequencies of the resulting FFT do not exactly match yet, the resulting phase difference already becomes smaller.


Although the performance is not perfect, the phase difference is clearly reduced. The remaining difference can be assigned to the resulting number of accelerometer samples. Because the sampling frequency becomes equal between the nodes, the number of frequency bins, and therewith their center frequencies, changes. The center frequencies between the nodes do not match anymore and a small phase deviation remains. To solve this last issue, extra samples are taken and the resampled time signal is simply cut to gain the same number of samples between nodes.

3.3.2 Accelerometer transient behavior

To properly determine the shape of a vibrating structure, all nodes should start their accelerometer measurement simultaneously in order for the system to be able to relate the determined local mode phase information of each node to that of the other nodes. However, in general, the measurements show a small time offset, caused by differences in transient behavior between the accelerometers. A number of nodes were placed together on the same vibrating platform and their measurements were started simultaneously, enforced by the reception of a trigger message from the CS. Figure 3 depicts the first data samples of a measurement performed at a sampling rate of 400 Hz. The resulting raw data shows an offset of one sample of, in this case, one particular node. This effect occurs randomly between different nodes. A reason for this offset could be assigned to the transient behavior of the accelerometer and/or to the external (asynchronous) clock of the accelerometer board in combination with the fact that the different accelerometer clocks are obviously not running synchronously. When initiating sample collection, the sampling clock could be just at the beginning or at the end of its next clock cycle. Measurements on the latency between trigger message reception and the call to start collecting accelerometer samples do not reveal any significant (random) time deviation between measurements. An important side note is that the maximum difference in start time is always only one sample, regardless of the sampling rate.

Figure 3: Raw accelerometer data of six nodes positioned on a vibrating platform excited with a sine wave with a frequency of 45 Hz. The plotted signals are the output signals of the accelerometers sampled at a frequency of 400 Hz. Node 2 incorrectly shows a time difference of one sample with respect to the other nodes, resulting in a phase difference. This effect randomly occurs for all nodes.

Nevertheless, one sample difference corresponds to a phase offset of 180° and 90° at a frequency of 200 Hz and 100 Hz respectively, when sampling at a sampling frequency of 400 Hz. Of course this is not


acceptable. This effect is strengthened when sampling at a low frequency in relation to the highest frequency of interest, which is half the sampling frequency in the current implementation. Hence, a solution would be to increase the sampling rate. This decreases the influence of one sample difference when utilizing solely the spectrum information up to 200 Hz.

To preserve the proposed frequency spectrum resolution of ~0.75 Hz, the number of collected samples should increase proportionally with the increase in sampling frequency. This would mean 4096 samples should be collected when increasing the sampling frequency to the maximum possible value of 3200 Hz. However, a memory issue arises here. The nodes currently offer an internal RAM size of 8192 bytes. For performing an in-place FFT, the amount of memory required equals to 4096 x 4 x 2 bytes. The factor of 4 is required because of the required 32-bit integer resolution and the factor 2 is introduced by the use of complex values. Allowing only input lengths that are powers of 2 which is a requirement of the implemented FFT routine, the maximum number of samples would be 512, taking into account RAM also used by the rest of the program. This, however, provides a maximum frequency resolution of 6.25 Hz which is far less than required.

The nodes also contain an external Flash memory chip and SRAM chip. The Flash chip supports slow (max. 60 kBps) reading and writing of memory blocks. This is not suitable when performing an FFT because of the lack of data locality in accessing the array of accelerometer samples. In practice this means that for every sample that is addressed on each FFT-instruction, an entire block of memory should be swapped which is not feasible. The SRAM chip supports faster (factor 10) byte addressable reading and writing which is more suitable and can be utilized for this purpose.

Still, the use of external memory for each operation in all algorithms, including the ones after FFT operation, is not convenient as it is very time- as well as energy consuming. A different solution is to use the resampling algorithm and downsample the sequence of accelerometer samples to the original sampling frequency of 400 Hz, just before applying the FFT. A resampling algorithm has high data locality, meaning that samples have to be loaded from external memory only once, as opposed to when applying an FFT on external memory. This comes with a number of advantages. First of all, downsampling the data decreases the amount of RAM memory required. Secondly, the FFT-input is smaller, which decreases the execution time. And finally we still manage to measure with a higher sampling frequency to reduce the influence of small asynchrony between the start of the measurement on the nodes.

3.3.3 Parameters

The nodes acquire 4096 samples at an (average) rate of 3200 Hz and downsample the results to an effective sampling frequency of 400 Hz. With the measures taken there is only a small phase difference remaining. The remaining phase deviation is frequency dependent and can be approximately described by ∆� = 0.002�[rad]. This means that the average phase differences between in-phase sensor nodes for vibrations of 50 Hz and 200 Hz (maximum frequency) are 0.1 and 0.4 radians respectively. This is about 1.6 and 6.4% of the unit circle which is small enough to derive a mode shape.

4 Example use case

Tests are performed to assess the quality of the results when combining the previously outlined algorithmic setup with a working solution in light of the application described in this paper. This chapter discusses the test setup and first results.

4.1 WIND TURBINE SETUP

The test setup is designed to perform modal analysis using Vibration Monitoring through the peak picking method on a scale model of a wind turbine. The deployed scale model has 9 wireless sensor nodes


attached, equally spread over its length of ~2m (Figure 4). A remote base node, connected to a PC (the Central Station) forms the gateway between the nodes and the user. The user is in control by initiating a measurement procedure from the PC. The small sensor network performs and processes a single measurement after which the base node transmits the resulting eigenfrequencies and mode shapes to the PC. The user on its turn is able to view the determined mode shapes in a Graphical User Interface (GUI). For verification purposes, a shaker is attached to the wind turbine to control the signal with which the wind turbine is excited. The natural eigenfrequencies of the wind turbine are known to be approximately 10, 101, 250 Hz and further.

Figure 4: Left: schematic image of a small-scale wind turbine with sensors attached. Right: picture of the used scale model of a wind turbine deployed with 9 sensor nodes evenly spread over its length of 2 m. A shaker is attached to the top of the scale model and excites the steel pole.

4.2 Results

Figure 5 shows the results of two different measurements when exiting the wind turbine with a single tone with a frequency equal to one of the eigenfrequencies of 101 Hz of the turbine [2]. The plot shows the values of the imaginary part of the FFT-outputs at 100.8 Hz on the Y-axis and the position of the node on the pole on the X-axis. Note that two nodes did not provide any results due to low battery power.

The mode shape that is plotted corresponds to the mode shape at a frequency of 100.8 Hz. This frequency is automatically picked by the nodes as being the frequency with the highest amplitude, which is correct, knowing that 100.8 Hz is the frequency closest to the excitation frequency of 101 Hz when having a frequency bin resolution of 0.78 Hz. The two measurements show a relative phase difference of the mode shape. This is directly related to the phase of the pole at the moment the measurement is started and is of no importance. The mode shape and measured peak frequency are in agreement with the expectations.

Figure 6 depicts three sub-plots of mode shapes of a measurement when exciting the pole with random white noise. The plotted mode shapes are the mode shapes at the three detected maximum vibration frequencies of 97.6, 100.0 and 102.3 Hz. The closely spaced modes are probably a result of the fact that the system is not perfectly axisymmetric. Also, the fact that the measurement time is only 1.28 seconds, the FFT output can be noisy, leading to more closely spaced peaks. The pole has eigenfrequencies around 10, 101, 250 Hz and further. As the frequency bandwidth of interest ranges from 0 to 200 Hz and the


intensity of the 10 Hz-peak is too low to detect with the current minimum peak-threshold setting, only the eigenfrequency around 101 Hz is detected.

Figure 5: Computed mode shapes in two measurements when exiting the wind turbine with a single tone of 101 Hz. The plot shows the value of the imaginary part of the FFT-output at 100.7 Hz on the Y-axis and the position of the node on the post on the X-axis, with the top of the post referring to 0 m and the bottom of the post to 1.8m. The imaginary values of the two measurements are normalized to the interval [-10..10].

Figure 6: Three measured eigenfrequencies and computed mode shapes for one measurement when exiting the wind turbine with random white noise. The depicted mode shapes are measured at a frequency of 97.6 Hz for the first sub-plot, 100.0 Hz for the second and 102.3 for the third. The plots show the values of the imaginary part of the FFT-outputs at the concerning frequency on the Y-axis and the position of the node on the post on the X-axis.


5 Full-scale network

The example of the small scale demonstrator shows the proper functioning of the distributed implementation of Vibration Monitoring (VM) in a small wireless sensor network using low-cost hardware. The aim is to scale this small network up to a larger network in order to monitor a full-scale wind turbine. This chapter will describe the architectural setup designed to facilitate the application of VM on this real-life structure.

5.1 Constraints

The application of VM in a large wireless sensor network brings two major practical constraints with it. Firstly, a limited availability of energy needs to be taken into account. The sensor nodes are not attached to an external power supply that can provide them with an infinite amount of energy. The nodes will only be equipped with a number of batteries. The longer the batteries last, the longer the network can operate without maintenance. Ways have to be found to limit the amount of consumed energy. Even if in the future almost infinite operational life might be possible by obtaining power by means of energy harvesting techniques, low power consumptions remains important to for instance limit the size of a solar panel. A second constraint is the limited communication range of the nodes. This means that the nodes are unable to send their data to the final destination through a direct link. Hence, this requires a network protocol that supports data hopping.

5.2 Architecture

In order to deal with the constraints mentioned, the following architecture is designed. Figure 7 depicts a schematic drawing of a 150 m wind turbine with the sensor nodes positioned on the tower of the turbine and the CS in the top. Note that the amount and positions of the nodes do not represent the final deployment. The maximum communication range in this example is estimated to be approximately 50 m. The nodes need to simultaneously start their measurement. As this is not feasible for the entire network using a direct link, the network is divided into clusters of nodes that each perform a local mode shape estimation. The clusters are not formed in advance but are automatically generated at each measurement. At each measurement iteration all nodes try to initiate a measurement by broadcasting a trigger signal at a random point in time; the node that tries first will be the temporary cluster head. All nodes that are in range of the temporary cluster head receive the trigger signal and will start the measurement. Another trigger signal from any other potential cluster will have no effect on these nodes anymore for the concerning measurement. In this way the cluster is able to determine a local mode shape. Figure 7 shows the generated clusters for an example measurement, in this case assuming that cluster 1 is created first in time, followed by clusters 2 and 3 respectively. This architecture increases portability and robustness as nodes can be added and removed (become unusable) without any significant consequences.

This approach leads to the state machine to be implemented on all nodes in the network, as depicted in Figure 8. For convenience, only the outgoing radio messages are indicated. State transitions depending on the reception of a message are indicated by the message identifier at the transition arrow. The base/sink node that is part of the CS will have no functionality remaining, other than forwarding the received mode shape information to the PC. Initially, the nodes are idle, i.e. in a very low-power state. Every measurement iteration, scheduled at fixed times, all nodes wake up and wait until they decide to become cluster head or receive a trigger from another cluster head. Depending on one of these two decisions they take the functionality of being the cluster head or being a measurement node. The nodes are applying low-power listening (LPL), a feature that senses the radio channel to detect ongoing radio traffic every x seconds (LPL_SLEEP_TIME) for a very short period of time. This saves a considerable amount of energy as opposed to continuously listening to the channel, as utilization of the radio is highly power consuming (in the order of 40 mW in listening mode).


Figure 7: Schematic drawing of a wind turbine with sensors (colored dots) placed on the tower. Clusters are formed that perform local mode shape computation. The non-dashed arrows indicate wireless data communication for cluster 1, distinguishing between direct links (intra-cluster communication in red) of local mode shape determination and multi-hop links (cluster-sink communication in black) for collection of results.

Figure 8: Schematic overview of the state machine of each node. A node can take the functionality of a cluster head or a sensor. The dashed arrows indicate outgoing radio messages. Incoming radio messages are left out of the figure from point of clarity. The abbreviations at the state transitions indicate the incoming radio messages that cause the transition.


Application of this scheme means that the transmitter must send (repeat) its message for a longer time to make sure the node receives its message. A consequence of this scheme is that the sensor nodes will not receive the message from the cluster head simultaneously. Hence, an additional state must be included in which the nodes sense the radio channel continuously to be able to start their measurement synchronously. In Figure 8 the Prepare Measurement and Start Measurement messages distinguish the transition between these two states. The application of LPL is still very useful because the nodes’ clocks will drift significantly from each other between two measurement iterations when performing, for example, one measurement per day. This means they will wake up at different moments in time, which would mean a lot of energy would be consumed if the radio is powered on continuously. Some extra maximum wait times between states are included to avoid that a node remains active until the next measurement in case it misses a message from the other node.

The transmission of the results between the cluster head and the CS must go through multiple hops. It is imaginable that there could also be a CS positioned at the lower end of the tower. In this case still, multi-hop remains a requirement. There are two main candidates suitable to be applied as multi-hop network protocol: flooding [4] and the Collection Tree Protocol (CTP) [3].

CTP is a unicast protocol in which nodes can forward their data to one or more root(s)/sink(s) that is (are) located at a position anywhere in the network of which the nodes are not directly aware. This particular application exactly matches that functionality. CTP is a recursive protocol in which nodes only have knowledge of the shortest path between their direct neighbours and a sink. Based on this information and the information about the quality of the direct link with their neighbours, they determine their shortest path to a sink. Each node broadcasts its own routing information to update their neighbours. As each sink is also part of the network ánd has a path of zero hops to the nearest sink (itself), the routes can be established.

In the case of (limited) flooding each node re-broadcasts every message it received, unless it already has broadcast the message in the near past or unless the particular message has already been broadcast by other nodes a predefined maximum number of times. These measures are taken to avoid congestion in the network.

The main difference between the two protocols is that flooding needs no routing information maintenance (routing overhead), which reduces communication. On the other hand, when using a flooding protocol, every data message is transmitted an unnecessary amount of times by each node in the network, which on its turn increases the amount of communication. The decision for selecting one of the two can be found in assessing the balance between routing overhead (control data) and the amount of actual resulting data to be transmitted (user data). As the complexity of this assessment increases in a larger network and due to applying more complex routing protocols such as CTP, the next chapter will describe a modelling and simulation tool that can be used for assisting in this evaluation.

6 DynAA

Large sensor networks increase the complexity of architectures with respect to the number of nodes and the physical relation between nodes regarding connectivity. Together with the availability of more sophisticated communication protocols and hardware, the number of dependencies to determine the expected network performance grows to a point where we as users lose overview. This chapter will describe a tool that can assist in doing this and will help in optimizing architectural parameters to result with the maximum possible performance characteristics such as lifetime for the full-scale wind turbine network.


6.1 Introduction and capabilities

Dynamic Adaptive Multi-Sensor Networks (AMSN) Architectures (DynAA) [5], is an architecture analysis and optimization tool for large, distributed, networked and adaptive (embedded) systems. DynAA is a discrete-event simulation environment that is, among others, able to analyze and optimize runtime dynamic models of (sensor) networks with respect to architecture, hardware, (wireless) communication performance, task properties and allocation based on application-specific constraints and influenced by runtime emergent behavior. DynAA (Figure 9) exists of a basic core defining the simulation environment and contains models for tasks, communication, hardware and other entities. The DynAA library provides a number of well-known (OSI-layer) communication protocols, processors and peripherals. The structure of DynAA allows easy addition and/or modification of components to suit the application under test.

Figure 9: Block diagram representing the DynAA tool. Based on user inputs such as design space and requirements, DynAA is able to perform optimizations by running simulations with self-generated parameters and provide the most optimal model. The generated parameters can relate to sensor positions, task allocation, transmission speed, type and/or configuration of communication protocols etc.

On top of this, the user is able to define its own application model, selecting and connecting components and defining what to analyze and what to optimize, whether that resides in its own application layer, hardware, lower communication layers or any combination of these.

6.2 Example

To show the advantages of DynAA, its functionality will be illustrated with an example. In a later stage, when the final network layout is determined - that is required from an application point of view - DynAA will be applied to derive the optimal network protocol; optimize its parameters and make a lifetime expectation.

In the example, an assessment of the earlier mentioned balance between user data and control data (route maintenance) is made with respect to network lifetime. As the flooding protocol seems more suitable in applications where only little user data has to be transmitted to the sink and CTP provides a bigger advantage when the amount of data is significantly higher than the required amount of control data, the break-even point between the two will be searched. In the example, a simplified model of the use case is utilized. In this model, a number of 10 nodes is spread out randomly over the 100m-tower of a wind turbine. All nodes simultaneously run a task of half a second after which each node transmits its resulting bytes that must arrive at the sink at the top of the post. The number of bytes transmitted will be varied in


the simulations. The nodes apply Low-Power Listening as a radio power saving scheme. In the simulation the nodes are equipped with a model of the CC1101 radio [6] and apply CSMA/CA [7] as a MAC-layer protocol. The channel is modelled according to the characteristics given in Figure 10 based on [10] assuming a transmission power of 10 dBm. Remaining variables are set as follows:

- Radio power consumption: o RX: 40 mW o TX: 80 mW

- Node power consumption: o Active (computing): 9 mW o Idle: 0.1 mW

- CTP beaconing settings: o Start interval: 64 ms o Interval incrementation factor: 2 o Maximum interval: 1 second

- Maximum hop count flooding: 4 hops - Task iteration period: 20 s - Node power supply: 2 x AA-size battery

The amount of transmitted data is varied to assess its influence on the network lifetime for both protocols. Assuming redundancy is applied to the number of deployed nodes, the network lifetime is defined as the time during which 75% of the nodes is still functioning. Figure 11 depicts the simulation results.

Figure 10: Radio channel model used in the simulation. The plot indicates the path loss and received Signal-to-Noise ratio (left y-axis) as well as the Bit Error Rate (right y-axis) with respect to the node to node distance (x-axis), assuming a transmission power of 10 dBm.

From this figure it is clear that CTP introduces overhead and is not advantageous over flooding when having low (<250 bytes per iteration) data rates. When the amount of transmitted data increases, flooding becomes less efficient. This is as expected as every node rebroadcasts the data of each other node when applying flooding.

Note that the current metric for network lifetime is chosen to be the time during which at least 75% of the nodes is still operating. A thing that is not assessed here, however, is the overload at specific nodes that


form key relays; i.e. they are located near a sink or form the bridge between two sub-networks. With respect to the full-scale wind turbine network, this must also be taken into account when choosing a proper node deployment. DynAA can assist in analyzing and optimizing the locations of nodes from an operation point of view.

Figure 11: Lifetime expectation of the simulated network when utilizing two different routing protocols: flooding and the Collection Tree Protocol (CTP). Per protocol the amount of transmitted data per iteration is varied. The lifetime expectation is defined as the time during which at least 75% of the nodes is still operating.

7 Discussion and conclusions

Advanced maintenance strategies for large infrastructure assets have growing interest from a cost point of view. Monitoring the structural integrity provides important information for these strategies. Such a monitoring approach can be based on monitoring the dynamic properties by applying operational modal analysis. Some vibration based monitoring methods such as the frequency-domain Peak Picking for determining modal parameters, lend themselves for implementation on a wireless platform. A first step has been made towards a low-cost, low-maintenance way of performing Structural Health Monitoring using Vibration Monitoring, by means of the development of a working implementation of a wireless distributed sensor network for modal analysis using the Peak Picking method. In this solution, ways are developed to cope with the consequences of utilizing low-cost hardware and having limited processing power and memory. Further developments required are the implementation of the proposed network and processing architecture to support modal analysis on larger structures. The DynAA analysis and optimization tool can assist in implementation-specific communication optimization and making life-time expectations.

Acknowledgements

The research is performed in the framework of the TNO Enabling Technology Program Adaptive Multi Sensor Networks.


References

[1] A. Cunha, E. Caetano, F. Magalhaes and C. Moutinho, From input-output to output only modal identification of civil engineering structures, SAMCO Final Report, 2006.

[2] T.G.H. Basten, F.B.A. Schiphorst, Structural health monitoring with a wireless vibration sensor network, Proceedings of ISMA2012. International conference on Noise and Vibration Engineering, Leuven, 2012.

[3] O. Gnawali, R. Fonseca, K. Jamieson, D. Moss and P. Levis, Collection Tree Protocol, Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, New York, USA, Nov. 2009, pp. 1-14.

[4] M. Denko, W. Goddard, Limited Flooding Protocol for Mobile Ad hoc Networks, Proceedings of the 14th MSc/PhD Annual Conference in Computer Science, Golden Gate, South Africa, June 2009, pp. 21-24.

[5] J. de Oliveira Filho, Z. Papp, R. Djapic and J. Oostveen, Model-based Design of Self-adapting Networked Signal Processing Systems, Proceedings of the 2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems, Philadelphia, USA, Sept. 2013, pp. 41-50.

[6] Texas Instruments Incorporated, Low-Power Sub-1 GHz RF Transceiver, Dallas, USA, 2014.

[7] M. Calder and M. Sevegani, Modelling IEEE 802.11 CSMA/CA RTS/CTS with stochastic bigraphs with sharing, Formal Aspects of Computing, Vol. 26, Issue 3, Springer London, May 2014, pp. 537-561.

[8] Texas Instruments Incorporated, 2.4 GHz IEEE 802.15.4 / ZigBee-ready RF Transceiver, Dallas, USA, 2014.

[9] P. Levis, J. Polaster, R. Szewczyk et al., TinyOS: An Operating System for Sensor Networks, Ambient Intelligence part II, Springer Berlin Heidelberg, 2005, pp. 115-148.

[10] R. Djapic, J. van de Sande, Y. Toh and M. Djurica, Connectivity of Wireless Sensor Nodes in Metal Rich Environment: Expectations and Reality, EuCAP 2014 - The 8th European Conference on Antennas and Propagation, The Hague, The Netherlands, Apr. 2014.

[11] A.T. Zimmerman, M. Shiraishi, R. Swartz and J. Lynch, Automated Modal Parameter Estimation by Parallel Processing within Wireless Monitoring Systems, Journal of Infrastructure Systems, vol. 14, no. 1, pp. 102-113, 2008.


a wireless distributed sensor network with low-cost...

Documents