Using DWT Lifting Scheme for Lossless Data Compression in Wireless Body Sensor Networks

Azar, Joseph; Darazi, Rony; Habib, Carol; Makhoul, Abdallah; Demerjian, Jacques

doi:10.1109/iwcmc.2018.8450459

Cited by 23 publications

(16 citation statements)

References 17 publications

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“…Numerous aggregation and compression approaches that take advantage of the temporal correlation in the collected data have been proposed [10,11,12,13,14].…”

Section: Data Reduction In Iot Applicationsmentioning

confidence: 99%

“…The results show that this method was able to achieve a compression of up to 10 times on temperature data. In [12,13] Compressive Sensing (CS) and transform domain compression techniques that are usually used for images have been proposed as well for multivariate time series compression in IoT applications. In [15], the authors proposed a multisignal compression technique based on the theory of fuzzy transform.…”

Section: Data Reduction In Iot Applicationsmentioning

confidence: 99%

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An energy efficient IoT data compression approach for edge machine learning

Azar

Makhoul

Barhamgi

et al. 2019

Future Generation Computer Systems

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210

102

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Many IoT systems generate a huge and varied amount of data that need to be processed and responded to in a very short time. One of the major challenges is the high energy consumption due to the transmission of data to the cloud. Edge computing allows the workload to be offloaded from the cloud at a location closer to the source of data that need to be processed while saving time, improving privacy, and reducing network traffic. In this paper, we propose an energy efficient approach for IoT data collection and analysis. First of all, we apply a fast error-bounded lossy compressor on the collected data prior to transmission, that is considered to be the greatest consumer of energy in an IoT device. In a second phase, we rebuild the transmitted data on an edge node and process it using supervised deep learning techniques. To validate our approach, we consider the context of driving behavior monitoring in intelligent vehicle systems where vital signs data are collected from the driver using a Wireless Body Sensor Network (WBSN) and wearable devices and sent to an edge node for stress level detection. The experimentation results show that the amount of transmitted data has been reduced by up to 103 times without affecting the quality of medical data and driver stress level prediction accuracy.

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“…Numerous aggregation and compression approaches that take advantage of the temporal correlation in the collected data have been proposed [10,11,12,13,14].…”

Section: Data Reduction In Iot Applicationsmentioning

confidence: 99%

Section: Data Reduction In Iot Applicationsmentioning

confidence: 99%

An energy efficient IoT data compression approach for edge machine learning

Azar

Makhoul

Barhamgi

et al. 2019

Future Generation Computer Systems

Self Cite

210

102

View full text Add to dashboard Cite

show abstract

“…The update step guarantees that the smoothed approximation signal has the same average as the initial signal. For a detailed explanation of the lifting procedure, refer to [29].…”

Section: Faster Implementation Of the Dwtmentioning

confidence: 99%

Robust IoT time series classification with data compression and deep learning

et al. 2020

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Internet of Things (IoT) and wearable systems are very resource limited in terms of power, memory, bandwidth and processor performance. Sensor time series compression can be regarded as a direct way to use memory and bandwidth resources efficiently. On the other hand, the time series classification has recently attracted great attention and has found numerous potential uses in areas such as finance, industry and healthcare. This paper investigates the effect of lossy compression techniques on the time series classification task using deep neural networks. Furthermore, this paper proposes an efficient compression approach for univariate and multivariate time series that combines the lifting implementation of the discrete wavelet transform with an error-bound compressor, namely Squeeze (SZ), to attain an optimal trade-off between data compression and data quality.

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“…All of the above reasons represent the motivation behind this proposed approach, as well as many other research ideas, which targeted this problem by implementing different data compression methods. These methods are used to decrease the overall data transmitted over the networks; this leads to a decrease in the sensors' energy consumption [7,8]. Compressing the data before the transmission process also leads to saving the bandwidth of the network; thus, the available resources can be used efficiently [9].…”

Section: Introductionmentioning

confidence: 99%

Internet of things data compression based on successive data grouping

Sawalha¹,

Al‐Naymat²

2021

Turk J Elec Eng & Comp Sci

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Internet of things (IoT) is a useful technology in different aspects, and it is widely used in many applications; however, this technology faces some major challenges which need to be solved, such as data management and energy saving. Sensors generate a huge amount of data that need to be transferred to other IoT layers in an efficient way to save the energy of the sensor because most of the energy is consumed in the data transmission process. Sensors usually use batteries to operate; thus, saving energy is very important because of the difficulty of replacing batteries of widely distributed sensors. Reducing the total amount of transmitted data from the perception layer to the network layer in the IoT architecture will save the energy of the sensor. This paper proposes a new IoT data compression method; it is based on grouping similar successive data together and sending them as one row with a total number of occurrences. The decision of similarity is done by comparing the root-mean-square successive difference calculated on the training dataset. The evaluation of the proposed method was performed on the Intel Lab dataset and the compression performance of the proposed method was compared with other compression methods, where a great enhancement was achieved; the compression ratio was 10.953 with a reconstruction of temperature data error equal to 0.0313°C.

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Using DWT Lifting Scheme for Lossless Data Compression in Wireless Body Sensor Networks

Cited by 23 publications

References 17 publications

An energy efficient IoT data compression approach for edge machine learning

An energy efficient IoT data compression approach for edge machine learning

Robust IoT time series classification with data compression and deep learning

Internet of things data compression based on successive data grouping

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