Walsh transform with moving average filtering for data compression in wireless sensor networks

El-Sayed, Mohamed A.; Mahmuddin, Massudi; Badawy, Ahmed; Elfouly, Tarek; Mohamed, Amr; Abualsaud, Khalid

doi:10.1109/cspa.2017.8064964

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…This approach is not suitable, however, in deployments that use floating point variables, which complicate direct application to IoT sensor measurements. Another useful mathematically-based compression method is the Walsh transform, which is very suitable for application to biosignals, but its applicability to smart cities or environmental data has not yet been demonstrated [10].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors

Konecny

Prauzek

Borova

2023

IEEE Internet Things J.

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The study presents a novel edge computing (EC) method based on a discrete wavelet transform (DWT) and fuzzy logic controller suitable for application with energy harvesting IoT sensors. The authors propose a new solution to address information latency in an IoT device when compressed data with high information density are transmitted to the cloud with high priority or detailed information is added to the cloud when the energy state in the IoT device is sufficient. The solution potentially delivers a completely lossless scenario for low-power sensors, a significant benefit that state-of-the-art methods do not provide. The article describes the hardware model for an IoT device, input and predicted energy data, and a methodology for designing the parameters of DWT and fuzzy logic controllers. The results of the study indicate that the proposed EC method achieved full data transmission in contrast to the reference solution which had the worst case parameters of maximum outage and penalties caused by delayed data. The average delay in uploading approximate data was 0.51 days with the proposed fuzzy controller EC method compared to reference methods, which have an average delay of at least 0.91 days. The results also highlighted the importance of the trade-off between information latency and reliable functionality. The results are discussed in terms of an innovative approach which features an IoT sensor that maximizes its own energy consumption according to the data measured from specific parameters.

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Section: Related Workmentioning

confidence: 99%

“…Section V reports the results of the experiment, discussing both the time domain and data availability. Section VI discusses and Huffman coding and discrete cosine transforms [8] • Online compression without the need for historical data [9] • Walsh transform with moving average filtering [10] Soft computing…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors

Konecny

Prauzek

Borova

2023

IEEE Internet Things J.

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“…Thus, to analyze an ECG signal that generates big data, a fast processing speed and data compression is required [4]. The existing signal compression studies used various compression techniques, such as the Fourier transform [5], wavelet transform [6], [7], Walsh transform [8], and Karhunen-Loeve transform [9]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient FPGA Accelerator With Fidelity-Controllable Sliding-Region Signal Processing Unit for Abnormal ECG Diagnosis on IoT Edge Devices

Lee

et al. 2021

IEEE Access

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Recently, with an increase in the number of healthcare devices, studies measuring and diagnosing electrocardiogram (ECG) signals in daily life are emerging. ECG signal analysis is an essential study area that can diagnose fatal heart abnormalities in humans at an early stage. Conventional signal detection uses one reference beat to diagnose ECG signals; thus, the detection rate is different for each person. In this study, we design a system that can learn a reference beat and diagnose ECG signals in real-time using hardware accelerators with the approximated template-based ECG diagnosis algorithm proposed in the previous study. The proposed algorithm can easily perform personalized learning, increasing the detection rate since it has faster learning time and consumes less memory than the existing algorithm. The learning data, which occupies a small memory space, enables real-time and simultaneous diagnosis of several people. We confirmed that the proposed ECG diagnosis algorithm is suitable for hardware acceleration by accelerating the ECG signal diagnosis and measuring the parallelized result using Alveo field-programmable gate array (FPGA). The ECG diagnosis algorithm, implemented at the FPGA in real-time, can flexibly determine reference beats that vary depending on the person and diagnose each person's signal. The experimental results showed that the time required to diagnose the ECG signals of five people containing 1987 beats takes 5.70 s with software and 0.572 s with hardware accelerators, which is 89.96% shorter than software execution time.

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“…Therefore, signal compression techniques are required to effectively store and transmit the data. However, conventional signal compression techniques, such as the Fourier transform, Walsh transform [11], wavelet transform [12], [13] and Karhunen-Loeve transform [14], result in loss in during the data compression process. In particular, signal distortion causes the nondetection or false detection of the fiducial point [15].…”

Section: Introductionmentioning

confidence: 99%

Advanced Real-Time Dynamic Programming in the Polygonal Approximation of ECG Signals for a Lightweight Embedded Device

et al. 2019

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Arrhythmia is less frequent than a normal heartbeat in an electrocardiogram signal, and the analysis of an electrocardiogram measurement can require more than 24 hours. Therefore, the efficient storage and transmission of electrocardiogram signals have been studied, and their importance has increased recently due to the miniaturization and weight reduction of measurement equipment. The polygonal approximation method based on dynamic programming can effectively achieve signal compression and fiducial point detection by expressing signals with a small number of vertices. However, the execution time and memory area rapidly increase depending on the length of the signal and number of vertices, which are not suitable for lightweight and miniaturized equipment. In this paper, we propose a method that can be applied in embedded environments by optimizing the processing time and memory usage of dynamic programming applied to the polygonal approximation of an ECG signal. The proposed method is divided into three steps to optimize the processing time and memory usage of dynamic programming. The first optimization step is based on the characteristics of electrocardiogram signals in the polygonal approximation. Second, the size of a data bit is used as the threshold for the time difference of each vertex. Finally, a type conversion and memory optimization are applied, which allow real-time processing in embedded environments. After analyzing the performance of the proposed algorithm for a signal length L and number of vertices N , the execution time is reduced from O(L 2 N) to O(L), and the memory usage is reduced from O(L 2 N) to O(LN). In addition, the proposed method preserve a performance of fiducial point detection. In a QT-DB experiment provided by Physionet, achieving values of-4.01 ± 7.99 ms and-5.46 ± 8.03 ms.

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Walsh transform with moving average filtering for data compression in wireless sensor networks

Cited by 9 publications

References 16 publications

Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors

Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors

Energy-Efficient FPGA Accelerator With Fidelity-Controllable Sliding-Region Signal Processing Unit for Abnormal ECG Diagnosis on IoT Edge Devices

Advanced Real-Time Dynamic Programming in the Polygonal Approximation of ECG Signals for a Lightweight Embedded Device

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