A Combined Approach for Real-Time Data Compression in Wireless Body Sensor Networks

Giorgi, Giada

doi:10.1109/jsen.2017.2736249

Cited by 31 publications

(34 citation statements)

References 18 publications

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“…In constrained edge devices, it is crucial to optimize resource usage. This means to optimize computational capacity, energy consumption and bandwidth usage [8]. These devices are often connected to the internet via wireless connection.…”

Section: Lightweight Compression Methods For Sensor Datamentioning

confidence: 99%

“…Kalman filter), which predict the data values from previous samples. In this method, the same filter is used in both sides of the network (sensor node and the user node where the data is analyzed further), thus the same estimation is used in both sides, and the new data is sent only if the value differs from the predicted value more than the tolerance level [8].…”

Section: Lossy Methods and Lossless Methodsmentioning

confidence: 99%

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Compression Methods for Microclimate Data Based on Linear Approximation of Sensor Data

Väänänen

Hämäläinen

2019

Lecture Notes in Computer Science

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Edge computing is currently one of the main research topics in the field of Internet of Things. Edge computing requires lightweight and computationally simple algorithms for sensor data analytics. Sensing edge devices are often battery powered and have a wireless connection. In designing edge devices the energy efficiency needs to be taken into account. Pre-processing the data locally in the edge device reduces the amount of data and thus decreases the energy consumption of wireless data transmission. Sensor data compression algorithms presented in this paper are mainly based on data linearity. Microclimate data is near linear in short time window and thus simple linear approximation based compression algorithms can achieve rather good compression ratios with low computational complexity. Using these kind of simple compression algorithms can significantly improve the battery and thus the edge device lifetime. In this paper linear approximation based compression algorithms are tested to compress microclimate data.

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Section: Lightweight Compression Methods For Sensor Datamentioning

confidence: 99%

Section: Lossy Methods and Lossless Methodsmentioning

confidence: 99%

Compression Methods for Microclimate Data Based on Linear Approximation of Sensor Data

Väänänen

Hämäläinen

2019

Lecture Notes in Computer Science

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“…Therefore, researchers have proposed various healthcare systems based on remote sensing and Hadoop dedicated to disease diagnosis, emergency detection, patient classification, etc. [1][2][3]. In [4,5], the authors gave an overview on different data analytical algorithms and big data platforms proposed in the literature for healthcare applications.…”

Section: Related Workmentioning

confidence: 99%

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

Harb

Mroue

Mansour

et al. 2020

Sensors

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Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients' classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.

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“…A Chaotic Compressive Sensing (CCS) algorithm was proposed in [10] to solve both problems of energy saving and data security. A combined compression algorithm [4] provided different biomedical signals that showed a significant improvement in the compression ratio with a small maximum error for optimizing the network resource usage over Wireless Sensor Networks (WSN). A lossy compression algorithm based on online dictionaries provided a quantitative assessment for compression, reconstruction and energy consumption of wearable Internet of Things (IoT) was presented in [11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Traditional EEG monitors and recorders transmit EEG signals via cables. In recent years, the Wireless Body Sensor Network (WBSN) technology is being widely developed since it can greatly enhance the way people live in terms of comfort and convenience [4][5][6]. WBAN is also being developed due to its high potential to replace the use of batteries for portable devices [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

Chen

Abu

et al. 2018

Applied Sciences

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Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity.

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A Combined Approach for Real-Time Data Compression in Wireless Body Sensor Networks

Cited by 31 publications

References 18 publications

Compression Methods for Microclimate Data Based on Linear Approximation of Sensor Data

Compression Methods for Microclimate Data Based on Linear Approximation of Sensor Data

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

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