Fast and efficient lossless adaptive compression scheme for wireless sensor networks

Kolo, Jonathan Gana; Shanmugam, S. Anandan; Lim, David Wee Gin; Ang, Li-Minn

doi:10.1016/j.compeleceng.2014.06.008

Cited by 33 publications

(8 citation statements)

References 15 publications

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“…Even though the LEC algorithm outperforms other algorithms, its setback of not being able to adapt to changes in sensed data was addressed by 'Adaptive Lossless Data Compression algorithm (ALDC) proposed by [2]. A fast and efficient Lossless Adaptive Compression Scheme (FELACS) [3] was also developed to address challenges faced by most of these algorithms. Based on Golomb-Rice coding, FELACS encodes data much faster than static Huffman, adaptive Huffman, and Arithmetic coding schemes.…”

Section: A) String-based (Text) Compression Techniquesmentioning

confidence: 99%

Data Compression Algorithms for Wireless Sensor Networks: A Review and Comparison

Ketshabetswe

Zungeru²,

Mtengi

et al. 2021

IEEE Access

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Energy consumption has risen to be a bottleneck in wireless sensor networks. This is caused by the challenges faced by these networks due to their tiny sensor nodes that have limited memory storage, small battery capacity, limited processing capability, and bandwidth. Data compression has been used to reduce energy consumption and improve network lifetime, as it reduces data size before it can be forwarded from the sensing node to the sink node in the network. In this paper, a survey and comparison of currently available data compression techniques in wireless sensor networks are conducted. Suitable sets of criteria are defined to classify existing data compression algorithms. An adaptive lossless data compression algorithm (ALDC) is analyzed through MATLAB coding and simulation from the reviewed data compression techniques. The analysis aims to discover strategies that can be used to reduce the amount of data further before it is transmitted. From this analysis, it was discovered that encoding residue samples, rather than raw data samples, reduced the bitstream from 112 bits to a range of 30 to 36 bits depending on the sample block sizes. The average length of data samples to be passed to the encoder was minimized from the original 14 bits per symbol to 1.125 bits per symbol. This demonstrated a 0.875 code efficiency or redundancy. It resulted in an energy saving of 67.8% to 73.2%. This work further proposes a data compression algorithm that encodes the residue samples with fewer bits than the ALDC algorithm. The algorithm reduced the bitstream to 26 bits. The average length of the code is equal to the entropy of the data samples, demonstrating zero redundancy and an improved energy saving of 76.8% compared to ALDC. The proposed algorithm, therefore, shows improved energy efficiency through data compression.

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Section: A) String-based (Text) Compression Techniquesmentioning

confidence: 99%

Data Compression Algorithms for Wireless Sensor Networks: A Review and Comparison

Ketshabetswe

Zungeru²,

Mtengi

et al. 2021

IEEE Access

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“…In this D-LZW algorithm, the authors aimed to find the differences between two given data samples before feeding the data into the newly designed algorithm. In [22], the authors lighted on the limitations of two state-of-the-art compression techniques, namely, LEC and S-LZW. Here the authors briefed that the LEC algorithm is a less robust technique that it cannot deal with the changing statistics of the source data sample.…”

Section: Introductionmentioning

confidence: 99%

A Robust Clustering-based Lossless Data Compression Technique for Air Quality Monitoring System

Ghose

Rehena

2022

Preprint

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With the advent of the IoT and Big data, there is an enormous supply of datasets, which are captured by the sensors installed in the relevant places. For the air pollution monitoring system, several sensors provide a huge dataset. They continuously capture pollutant concentration as well as the meteorological factors to form the voluminous, multivariate time-series dataset, which includes both. Those sensors are paired with low-capacity batteries and limited memory, transmission, and computational components. Transmitting the high volume data to the monitoring system deployed in the cloud demands high energy dissipation and higher bandwidth. The single solution for all these constraints is data compression. Data compression reduces the volumes of the datasets. The reduced volume of data saves energy and it is easier to transmit the compressed data through the limited bandwidth. In this paper, a lossless data compression algorithm is proposed. On successful implementation of the algorithm, the data is compressed with absolutely no data loss. To evaluate the efficiency of the algorithm, its performance is compared with some classical and several state-of-the-art compression schemes. The experimental results show that the proposed compression algorithm outperforms the classical as well as state-of-the-art models concerning the performance evaluating parameters like Compression Ratio, Compression Factor, and most importantly Power Saving.

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“…Satellite and medical imagery are often high-resolution and at times require the original data to be protected from any loss. Different types of images such as grayscale, multi-spectral images, hyperspectral images, electrocardiogram (EEG), and Magnetic Resonance Imaging (MRI) have been processed to achieve lossless compression [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression

Waleed

Khan

et al. 2020

Applied Sciences

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Reversible Colour Transforms (RCTs) in conjunction with Bi-level Burrows–Wheeler Compression Algorithm (BBWCA) allows for high-level lossless image compression as demonstrated in this study. The RCTs transformation results in exceedingly coordinated image information among the neighbouring pixels as compared to the RGB colour space. This aids the Burrows–Wheeler Transform (BWT) based compression scheme and achieves compression ratios of high degree at the subsequent steps of the program. Validation has been done by comparing the proposed scheme across a range of benchmarks schemes and the performance of the proposed scheme is above par the other schemes. The proposed compression outperforms the techniques exclusively developed for 2-D electrocardiogram (EEG), RASTER map and Color Filter Array (CFA) image compression. The proposed system shows no dependency over parameters like image size, its type or the medium in which it is captured. A comprehensive analysis of the proposed scheme concludes that it achieves a significant increase in compression and depicts comparable complexity similar to the various benchmark schemes.

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Fast and efficient lossless adaptive compression scheme for wireless sensor networks

Cited by 33 publications

References 15 publications

Data Compression Algorithms for Wireless Sensor Networks: A Review and Comparison

Data Compression Algorithms for Wireless Sensor Networks: A Review and Comparison

A Robust Clustering-based Lossless Data Compression Technique for Air Quality Monitoring System

On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression

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