Wavelet Transform for Signal Processing in Internet-of-Things (IoT)

1. Introduction

Internet of Things (IoT) refers to a network of diverse range of smart devices used in the domains of healthcare, industry, vehicles, homes, agriculture, retail, poultry and farming, and many more. Typical equipment supporting the IoT functionality include lightning, thermostats, TVs, sensors, mobile phones, speakers, voice assistants, cameras, video cameras, etc. These devices are basically deployed to facilitate the processes of monitoring and automation by transmitting and receiving information via internet. Undoubtedly, IoT has emerged as a rapidly growing ecosystem that promises to deliver unmatched global coverage, quality-of-service (QoS), scalability, security and flexibility to handle different requirements for a comprehensive list of use-cases. This has resulted in increasing number of IoT devices (relays, sensors, transceiver, actuators etc.) being deployed in in all types of urban, suburban and rural environments to cater to the innovative and emerging applications.

Since more devices and appliances have been transforming into their smarter version, we now have the applications such as smart cars with features of smart dashboards, GPS, smart doors and auto-route designed to reduce the accidents. Such applications clearly require high number of connected devices; in fact, it has been forecasted by International Energy Agency that the estimated number of connected devices which was 15 billion in 2018 shall reach 46 billion in 2030 [1]. In addition to the IoT devices, the evolution of IoT networking technologies has also been remarkable over the past decade, where more and more IoT devices have been shifting from using Long Term Evolution (LTE) to Narrowband-IoT (NB-IoT) which offers a cost-effective and energy efficient solution for continued operation of these systems. Naturally, the connected devices are expected to transmit large volumes of heterogeneous data at high data rates, and we will be required to deal with ever-increasing radio frequency noise.

The signals carrying IoT data are highly likely to face numerous obstacles and can be corrupted by significant amount of noise present in the environment. White Gaussian model has been commonly been used to quantify the noise faced by [1]. The types of noise which have been found to degrading the quality of IoT signals vary from the impact noise resulting from high frequency interference and instantaneous disturbance on the initialization of large equipment to changing connections around the participating IoT devices [2]. All these kinds of noise negatively influence the multi-device information fusion system [3]. Such noises should be filtered out and the transmitted signal should be reconstructed back to its actual form to ensure the accuracy and reliability of the transmitted information. Here, accuracy of IoT solutions is measured in terms of the number of packets reporting correct information, deviation between the reported and actual results and the delivery to correct destination timely. Similarly, the reliability of IoT is measured using information such as failure rate of the IoT devices, average time between two consecutive failures, average repair time and probability for needing to change a component within a certain time-frame.

Although this chapter mainly deals with algorithms for signal denoising, they can be also be applied for image denoising, as images can be represented as two-dimensional signals. Consequently, signal processing techniques applicable to signals can be modified for images.

2. Noise consideration

The process of removing the noise while retaining and not distorting the quality of the received signal or image is referred to as denoising. The traditional way of denoising is to use a low or band-pass filter with cut-off frequencies. However, the traditional filtering techniques are able to remove out-of-band noise. Therefore many denoising techniques are proposed to overcome this problem.

Denoising is also an indispensable link in speech signal processing owing to the varying origins and non-stationarity, and difficulty in modeling the noise affecting the signal. Assuming that the received signal is affected by white additive Gaussian noise (AWGN) which is also stationary in nature, the received signal yican be represented as,

where xiis the noise-free transmitted signal, εirepresenting independent normally distributed random variable and σrepresenting the intensity of the noise affecting yi. Reconstruction of the original signal xifrom the instantaneous set of yivalues without actual assuming a specific model for xior yiis the primary aim of the process of ‘Denoising’. The most common approach is to recognize noise components as the high frequency components present in the corrupted received signal, apply Fourier transform and then filter out the high frequency components. Therefore, the most traditional way of denoising signals is based on Fourier analysis and Fourier transform.

Another common denoising method is the modulus maxima method [4]. It is based on the concept that signal and noise exhibit different characteristics when projected to their maxima in space divided in multiple scales. Magnitude scales increasing with decreasing extreme value points are filtered out to remove noise and the extreme value points themselves are reconstructed back [5]. The modulus maxima method in addition to the noise effect is better than any other method when mixed with white noise and singular information is significant, but the computational complexity is quite high. However, Fourier transform based denoising is restricted due to its weakness in obtaining partial characteristic of the transmit signals and possible Gibbs phenomenon [6]. If the signal has the same frequency as the noise, filtering out those frequency components will cause noticeable loss of information of the desired signal when considering the frequency representation of the signal.

3. Wavelet transform

Wavelet Transform (WT) has emerged as a powerful tool for signal and image denoising and processing, that have been successfully used in many scientific fields such as signal processing, image compression, computer graphics and pattern recognition [7, 8]. On contrary to the traditional Fourier transform, WT is particularly suitable for application of non-stationary signals which may instantaneously vary in time. Primarily, the received signal is divided into different frequency components using wavelets. The basis function of WT is scaled based on frequency and a subset of small waves (known as mother wavelet) is used for implementing WT [9]. The mother wavelet is a time-varying window function used for decomposition of xiinto weighted sets of scaled versions of yi. Consequently, using wavelet transform in signal processing is the process of the partial transformation of the spatial domain and the frequency domain, in order to get useful information accurately from it though corrupted with noise.

Since different frequency levels are used for WT, it is quite convenient for analyzing the signal characteristics at different frequencies and detecting removing corrupting noise. Broadly, there are two types of WT, Continuous WT (CWT) and Discrete WT (DWT).

3.2 Discrete wavelet transform (DWT)

If suitable transformation is applied to a group of selected wavelet, a collection of orthogonal real-valued wavelets will be generated, a representation of the received signal referred to as wavelet expansion. In this case, the properties of the generated wavelets depend on the features of the mother wavelet. Since the newly generated wavelets are a group of orthogonal wavelets, they provide a time-frequency localization of the actual input signal, thereby concentrating the signal energy over a few frequency coefficients. Scaling and translation of the mother wavelet generated. If the scaling factor is a power of two, the wavelet transform technique is referred to as the dyadic-orthonormal wavelet transform [10]. If the chosen mother wavelet has orthonormal properties, there is no redundancy in the discrete wavelet transforms. In addition, this provides the multiresolution algorithm decomposing a signal into scales with different time and frequency resolution [9].

DWT is an implementation of WT using mutually orthogonal set of wavelets defined by carefully chosen scaling and translation parameters (aand b), such that the normalized area between the analyzing functions is unity, leading to a very simple and efficient iterative scheme for doing the transformation [11]. The translation equation can be expressed as,

DWTnaj=∑m=0N−1xmψj∗m−n;ψjn=1ajψnajE3

where nis the time delay introduced, Nis the signal length and ψis the discrete mother wavelet windowing function. DWT operates on discrete wavelet sets thereby yielding signal compression and reducing the computational complexity considerably. Moreover, DWT provides better spatial and frequency localization, as compared to other multi-scale signal maxima representation, thereby eliminating redundancy. In DWT, signal is decomposed into ‘approximation’ and ‘detail’ coefficients at each level [12].

The process is repeated at multiple levels, a technique equivalent to consecutive iterations of low pass and high pass filtering. As a result, the low frequency and high frequency components of xtyield the approximation and detailed coefficients respectively, which can be mathematically expressed as,

xt=∑m=1L∑k=−∞∞Dmkψm,kt+∑k=−∞∞Alkϕl,ktE4

Where Dmkis the detailed coefficient, Alkis the approximation coefficient, ψm,ktis 2m-scale discrete analyzing function, and ϕl,ktis the 2l-scale scaling function. After scaling and wavelet filtering, we get [13].

hn=2−1/2<ϕt,ϕ2t−n>gn=2−1/2<ψt,ϕ2t−n>=−1nh1−nE5

The approximation and the detailed coefficients are compared by applying FIR filter bank. The filter bank uses a low-pass filtering hfor generating the approximation coefficients and high-pass filtering gfor generating the detailed coefficients, followed by down-sampling by a factor of 2 at each scale level. The entire process is referred to as sub-band coding. The resultant tree structure is presented in Figure 1, where, ↓2and ↑2represents the processes of down-sampling and up-sampling respectively. The DWT decomposition process can be applied on both sub part of the signal, approximation coefficients and detail coefficients. This kind of decomposition is referred to as wavelet packet transform or wavelet packet tree decomposition. Figure 2 represents the wavelet packet decomposition and reconstruction process.

4. DWT for denoising data

The DWT denoising procedure consists of three steps. In the first step, if the length of the data stream is of length of the order of power of two, it is transformed to the wavelet domain. In the second step, coefficients with either zero magnitude or criterion-based minimized values are selected. In the third or final step, the minimized coefficients are reverted back to the original domain from the wavelet domain to extract the denoised data. DWT-based denoising techniques can be broadly classified into two categories - linear and non-linear. In linear DWT, signal and noise are assumed to be belonging to the smooth and the detailed part of the wavelet domain, where high frequency components are attenuated. While in non-linear DWT, the filter removes the coefficients selected in the second stage with amplitudes less than the threshold. In practicality, non-linear DWT is always preferred over linear DWT, as linear DWT introduces error due to the retention of noise components and loss of signal components owing to wavelet filtering.

Whether linear or non-linear DWT denoising technique is used, performance depends on the choice of the wavelet family and the length of the filter. The traditional way for making this choice is based on visual inspection of the data, for example, daublets are implemented when the data appears smooth in the wavelet domain, while Haar or other wavelets are used when the data appears bursty and discontinuous in the wavelet domain. In order to overcome the problems with DWT denoising, correlation denoising method was introduced in [11]. Correlation denoising method implements wavelet transformation and filtering in a way such that the correlation between wavelet coefficients of the signal part and the noise part is different at each level. However, correlation denoising in its original form is computationally complex. In order to reduce computational complexity, wavelet threshold denoising method was proposed by [12]. The method is simple to calculate and the noise can be suppressed to a large extent. At the same time, singular information of the original signal can be preferred well, so it is a simple and effective method. A brief overview of what happens when DWT is applied for denoising is demonstrated in Figure 3.

The four major components of the DWT denoising technique are: wavelet-type selection, threshold selection, threshold function selection and threshold application to the wavelet coefficients.

1. Wavelet Selection- There is a wide variety of wavelets that can be used for denoising. Selecting the optimum one depends on the selection of the matching wavelet filter. Out of different wavelet transform based denoising methods, only minimum description length (MDL) method has the flexibility of choosing the filter type.

2. Threshold Selection- There are four basic types of threshold selection, mini-max, Stein’s unbiased estimate of risk (SURE), and minimum description length (MDL). The Universalthreshold is computed using,

for which Nis the length of the signal data array, and σis the standard deviation of noise. In practicality, in most cases, σis unknown, but can be estimated using the first detailed part of the wavelet coefficient xithrough the expression,

In the case of Minimaxcriterion using the estimates of the minimax risk bounds for the transformed wavelets, a table is generated for threshold values corresponding to each set of given data lengths. These threshold values are always smaller than the universal threshold. The noise level estimates are calculated using (8) and signal components are retained along with a few number of noise components.

Stein’s unbiased estimate of risk (SURE)is used to obtain an unbiased estimate of the variance between the filtered and unfiltered data. SURE is defined as

for which t, xi, Nand Mrefer to the candidate threshold, wavelet coefficient, data length and number of data points less than t. The value of tthat minimizes the SURE value is selected as the threshold value while the final term of the SURE function represents the residual energy left after thresholding. The SURE threshold can be modified to yield global thresholds rather than local ones by combining SURE method with cycle-spinning technique; a method referred to as SPINSURE.

The Minimum description length (MDL)method for threshold computation can be expressed as,

for which k, m, xm, and xmkrepresent the number of largest coefficients retained after filtering, the filter type, the wavelet coefficients from m-type wavelet transform, and the klargest coefficients in amplitude respectively. Here k∗and m∗are the optimized values for the MDL criterion for threshold selection, where k∗is selected as the threshold for the corresponding wavelet coefficient. The 3/2klogNterm represents the penalty function with value proportional to the number of retained wavelet coefficients. The N2∑xm2−xmk2characterizes the error between the reconstructed and the original signal components.

3.Selecting threshold function- whether wavelet threshold denoising method is good or bad depends on two decisive factors; one is the threshold λand the other important factor is the selection of the threshold function. The most basic threshold functions are the hard and soft threshold functions, comparative performance of which is presented in Figure 4.

The Hard Threshold Function (HTF)nullifies the decomposition coefficients to zero if they are less than the threshold and retains the coefficients if they are more than the threshold [22]. The HTF preserves the local properties of a signal with a few discontinuities introduced by the variations in the reconstructed signals. HTF can be expressed as,

The Soft Threshold Function (STF)[23] selects the threshold value such that all decomposition coefficients are nullified to zero. A major drawback with this technique is that a part of the high frequency components is lost owing to their location above threshold. STF can be mathematically expressed as,

where ωj,k¯, ωj,k, λ, and sgn() denotes the estimated wavelet coefficients, post-decomposition wavelet coefficients, threshold and symbolic piece-wise function respectively [24].

Garrote Threshold Functionis proposed in [25] to improve the drawbacks of HTF and STF, whose denoising effect is better than the above two methods with respect to continuity of expressions,

The continuity in the soft threshold function is much better, but it has a constant deviation. So, in order to overcome its shortcomings, the soft and hard threshold algorithms are compromised process by the literature; the semisoft threshold function[26].

It is worth-mentioning here, that the values of the threshold Tis fixed with values between 0 and 1 in the case of HTF, STF, Garrote Threshold Function and semi-threshold function.

Another variation is the Improved Threshold Functionwhich can be given by,

The adjustment factor of the new function is different from the semisoft threshold function. It consists of a complex exponential function exp−3αωj,k−λ/λwhich has more adaptability; αis the normal number which can be adjusted freely and the values of αare different with the different signal. When ∣ωj,k∣=λ, ωj,k¯→λ, ωj,k¯→0.Therefore, continuously in place of λ, the improved threshold function has the characteristics of soft threshold function; when ωj,k¯→∞, ωj,k¯→ωj,kimproved threshold function based on ωj,k¯=ωj,kas the asymptotic line; it can be seen that, with the increase of ωj,k, ωj,kwill gradually be close to ωj,k; when ωj,kbecomes infinite, ωj,k≈ωj,k. The choice of αis crucial for the success of the technique and the variation in αaffects the denoising effect. When α=0, improved threshold function reduces to STF and when α=∞, improved threshold function reduces to HTF.

4.Thresholding or threshold application- thresholding is defined as the ways in which threshold is applied for modifying wavelet coefficients. DWT is a multi-level wavelet transform technique with different thresholds being applied at different level of coefficients

Global Thresholding- This technique assumes the corrupting noise as Gaussian distributed with amplitude and frequency distributions same for all orthogonal bases for the entire data space. Global thresholding can be implemented using either hard, soft, Garrote or firm-threshold functions, expressed as,

• Hard:

• Soft:

• Garrote:

• Firm:

for which xiand xi∗represents the wavelet coefficients pre- and post-thresholding respectively. HTF partitions the wavelet coefficients into two parts by the selected threshold eliminating coefficients with low magnitude. STF reduces all coefficients by a factor equal to the threshold eliminating smaller coefficients. Similarly, Garrote thresholding reduces all large coefficients by a factor of a non-linear continuous function. Firm thresholding reduces only the middle coefficients while eliminating small and retaining large coefficients.

Level-Dependent Thresholding- This technique uses different thresholds at each level of wavelet transformations. It uses a combination of SURE and global thresholding techniques to initiate a hybrid method. In this case, if the sample variance at each level is sparse, global thresholding is applied, while SURE thresholding is applied otherwise.

Data-Dependent Thresholding- A Data-dependent threshold (DDT) technique selects a threshold such that empirical wavelet coefficients are shrunk. The thresholding is achieved through statistical tests of hypotheses like linear regression. The level of this statistical test is adjusted to control the smoothness of the resulting estimator such that a good mean-squared error (MSE) performance is achieved for different data analysis settings with smoothness in estimator response. The main aim of this technique is to eliminate a group of wavelet coefficients that exhibit characteristics of pure noise.

Cycle-Spin Thresholding- It combines the process of subspace identification, projecting denoising and averaging of the projections. The subspace mentioned here refers to the region where most of the energy of the signal is concentrated and signal corrupted with noise is projected on to this subspace.

5. Signal denoising for IoT networks

The huge amount of sensor data generated in an IoT network are used to take decisions on a certain observation/ phenomenon based on real-time processing. The decision-making procedure often involves detecting the signal energy level transmitted from the sensors. If the received energy level is higher than a predefined threshold, the target is detected to be present phenomenon and vice-versa. However, the sensor data gets crippled with noise contributed by the wireless environment and the internal electronics of the sensors, on its way to the data center for processing. The WPT method will be the best option in this case for denoising the sensor data, where the original signal coefficients are preserved while removing the noise within the signal. The WPT method can decompose a signal in both scale and wavelet space thereby revealing more details about both the sensor signals and the crippling noise. If energy correlation analysis is used in conjunction with WPT, signal energy from the sensor data can be analyzed and noise can be eliminated by zooming into the signal characteristics at different time scales. Advantages of WPT over WT is evident in Figure 5. Hence, in this section, a universal framework is presented for denoising sensor signals in IoT networks. The framework is based on energy correlation analysis and combines the processes of WP decomposition, coefficient modification and WP reconstruction. The functional block diagram for this framework is presented in Figure 6.

5.3 Processing method for WP coefficients based on energy-correlation analysis

An online filtering process capable of denoising both Gaussian and impact noise is presented below based on the energy correlation between signal components reconstructed from WP coefficients.

Step 1 - Obtain WP decomposition coefficients through the application of appropriate decomposition level and mother wavelet.

Step 2 - Compare WP coefficients in each subspace to eliminate singular data based on a pre-selected threshold through the application of multi-resolution analysis.

Step 3 - After reconstructing WP node signals from real coefficients, compute the ratios of the energy of the reconstructed signal components to the actual signal components to obtain the correlation between them. Subspace unsatisfied coefficients are processed through the use of a different threshold resulting in a series of new coefficients.

Step 4 - Using the new set of modified coefficients on each node, signal components are reconstructed and noise is eliminated. If the filtering requirements are not satisfied, repeat steps to step 4 after increasing the decomposition level. A flow-diagram for energy correlation analysis based WP coefficient processing is depicted in Figure 7.

6. Performance analysis of denoising techniques

The best way to denoise a signal is to assume that the noise signal is Gaussian distributed with values that are independent and identical real values. The performance of the denoising process can be evaluated by comparing the quality of the denoised signal with that of the original transmit signal. A variety of methods have been proposed over years to measure the performance of denoising; the most common of which are the metrics of SNR and the peak SNR (PSNR), generally accepted to measure the quality of signal and images respectively. For 1-D signal, measuring the performance of the denoising method by calculating the residual SNR is given by, SNR=10log10∑n=0N−1x2n/∑n=0N−1x¯n−xrn2where xnis the original signal, xrnis the denoised signal and x¯nrefers to the mean value of xn.

In order to measure the quality of image, PSNR is generally used, which is given by PSNR=10log10L/∑n=0N−1∑m=0M−1x¯nm−xrnm2, where L, xn, x¯nmand xrnmrefer to the quantized gray level of images, original image, mean value of xnand the reconstructed image respectively. However, the choice of the noise power is absolutely crucial for visible performance difference. SNR is more important as compared to noise power when evaluating performance and with SNR above 3 dB, it is quite easy to isolate visible corruption.

7. Conclusions

Decomposition in time and frequency domain for Fourier Transform is replaced by decomposition in space domain for WT thereby removing any ambiguity related to time and frequency and offering high flexibility and quality to the overall denoising process. Different threshold estimation methods, wavelet types, threshold types and thresholding functions can be used for implementing WT depending on the application scenario, network architecture, the kind of signal transmitted and the kind of noise commonly observed in the considered application scenario. However, comparing performances of different thresholding methods, wavelet types or threshold types when applied for the WT reveal that the number of decomposition levels are more crucial to the denoising performance than the types of wavelets or thresholds.

If the application scenario is considered to be an industrial IoT network, WPT method is preferred over simple WT for denoising sensor signals. This is because in WPT, signal is decomposed into an approximation and a detail component at each layer of each decomposition level, therefore resulting in 2nnumber of components at ndecomposition levels in contrast to just 2 components at each of the ndecomposition levels of WT. Moreover, WT decomposes only the low frequency components in contrast to WPT which considers both low and high frequency components at each level. If WPT is combined with energy correlation analysis, effectiveness of the denoising process increases manifold owing to its immunity to diversity of signals in an IoT network. Integration of energy and correlation can be used to modify wavelet packet coefficients for eliminating Gaussian and impact noise efficiently.

A. Appendix A