Data-driven Approaches and WSNs

Because data-driven approaches are more relevant to the topic of this study, they are considered further in this section. This type of method is categorised into two tech-niques which aim to reduce the amount of data transmitted by utilising two different principles, as shown in Figure 17 [137]. The first approach achieves data reduction via the local pre-processing of raw data on-board on wireless units, so that only the results of evaluation and other meaningful information are transmitted to the remote control room; these are distributed WSNs [141]. Here the radio frequency transceiver of the wireless sensor node is the most power-hungry part, whereas the local processing of da-ta consumes much less energy [142, 143]. On the other hand, in dada-ta compression in-formation encoded on-board is sent to the sink where it is decoded with the aim of re-moving redundancy in the information [144]. Finally, data prediction is based on de-scribing the actual phenomenon sensed by a sensor node via the development of a spe-cific model executed at the sink [145].

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Figure 17: Taxonomy of data-driven approaches to energy conservation [137].

2.4.1.1 Energy-efficient DAQ Schemes

In contrast, energy-efficient DAQ schemes try to tackle the problem of energy conser-vation from another angle, by reducing the power consumed by the sensing subsystems, such as analogue-to-digital converter (ADC) devices which may consume much more power than radio transceivers [137, 146, 147]. This may be due to the power-hungry nature of such devices, their long acquisition times, and the use of active sensors. Nev-ertheless, these schemes also have a direct link to data reduction and therefore the min-imisation of the amounts of data transmitted.

As illustrated in Figure 17, this technique can be performed in the form of adaptive sampling, where the sampling frequency is adjusted based on a temporal analysis of the information acquired [148]. Unfortunately, this may lead to the problem of over-sampling if unnecessarily high over-sampling rates are set. A Kalman filter-based method is used to overcome such problems; however, this method requires the adaptive sampling to be calculated in a centralised unit which broadcasts it to the sensor nodes. Other adaptive techniques are discussed in more detail in the literature [137, 149].

Another form of energy-efficient DAQ approach is known as a hierarchical sampling routine or multi-scale sensing, in which a cluster of efficient power sensor nodes are used first to detect events; nonetheless, this process has limited resolution. Next, more advanced nodes are employed to perform a more detailed detection [150]. The level of

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accuracy of this approach is the result of a trade-off between resolution and power effi-ciency.

Model-based active sampling predicts the future values of a sensed phenomenon based on a model which is developed using an initial set of sampled data with known levels of accuracy [151]. By doing this, energy is saved in the next sampling process. As long as the accuracy gained becomes low, a new model needs to be estimated in order to follow changes in the physical phenomenon monitored [137].

Although these methods can contribute to data reduction and therefore a preservation of the communication bandwidth as well as the optimisation of power consumption, these methods may be unsuitable for dynamic events which exhibit rapid variation over time.

This is due to the significant communication overhead between the nodes and the cen-tral unit or sinks. This results in busying the wireless nodes which makes its response to other actions slow. In addition, multi-scale sensing adds more complexity to the moni-toring system, particularly for SHM applications. These techniques still follow the Nyquist theorem for sampling acoustic signals which is one of the most critical chal-lenges in WSNs. Thus, the use of sub-Nyquist sampling rates approaches is required in SHM based AE applications, which will lead to a reduction in both the amounts of data and power consumption, as discussed next.

2.4.1.2 Compressive Sensing Approach and WSNs

In several WSN-based applications, due to resource limitations part of the captured data is often discarded before being stored or transmitted in order that the signal can be com-pressed. In addition, ADCs are power-hungry devices, particularly if high sampling rates are used. Thus, the question that is raised is why the physical phenomenon to be monitored is not sampled at lower rates in order to save cost and time, if part of the data is going to be thrown away anyway. The answer to this question represents the basic principle of the recently emerging technique of compressive sampling or sensing (CS).

A recent extensive review lists the latest studies covering CS and its applications [152].

CS is an alternative theory to Nyquist criterion which is about recovering a signal or an image from a few random samples much less than what Nyquist criterion usually sug-gests. However, this is subject to the condition that signals or images satisfy the re-quirements of sparsity and compressible representation Donoho [153] and Candès et al.

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[154]. The CS relaxes the Nyquist criterion and allows the employment of low sampling rates in the process of the acquisition of data from the physical phenomenon. It has at-tracted a considerable number of researchers in different areas ranging from medical imaging, signal processing and seismology to communications and networking. Recent-ly, there has been a growing interest in applying the CS technique to a wider range of topics in SHM and the relevant NDT techniques in conjunction with WSNs.

Such an application has the advantages that it results in saving time and cost, since it does not work on the basis of first sensing then compressing, but instead performs com-pression while sensing at a lower sampling rate. Applying these principles in WSNs leads to overcoming the limited resources of WSNs, including storage, bandwidth, and power problems. This is why this approach is categorised among the energy-efficient data acquisition schemes, as shown in Figure 18.

Figure 18: Taxonomy of energy-efficient DAQ approaches with the new data reduction method.

Most of the works conducted in CS [155, 156] have the intention to perfectly recover the monitored physical phenomena from a small number of random projections sent over the wireless link, as will be discussed in Chapter 3. However, the reconstruction of these phenomena at the sink may lead to a degradation in quality of the recovering pro-cess due to variations in the data gathered if it is not manipulated appropriately [157].

Also, sending a number of random vectors to the central unit is still a challenge and makes the recovery process severe [158]. This is true for applications such as SHM, in which the recovery of the original signal or image may be unnecessary if data are local-ly processed and main features are extracted under the use of low sampling rates. In this case, these challenges can be overcome, as shown next.

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