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GreedyGD: Enhanced Generalized Deduplication for Direct Analytics in IoT

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 6954-6962页

作者： Hurst, Aaron Lucani, Daniel E. Zhang, Qi Aarhus Univ DIGIT Ctr DK-8200 Aarhus Denmark Aarhus Univ Dept Elect & Comp Engn DK-8200 Aarhus Denmark

The exponential growth of data generated by the Internet of Things presents significant challenges for data communication, storage, and analytics. Consequently, organizations often face high costs when attempting to leverage their own data. Novel techniques that holistically optimize data storage and analytics in IoT systems are therefore required. One promising approach is generalized deduplication (GD), which is a lossless compression technique that delivers high compression while also enabling low-cost random access directly on compressed data. In this article, we introduce GreedyGD, a novel GD data compression algorithm that offers reliable, efficient data analytics, along with more compression and faster runtime compared to previous GD compressors. Evaluating GreedyGD on 18 real-world datasets revealed excellent performance: a 11.2x speed-up, 1.6x more compression, and more accurate and reliable analytics while using 4x less data compared to previous GD compressors.

关键词： Costs Internet of Things Data analysis Reliability Data compression Compressors compression algorithms Clustering data analytics data compression generalized deduplication IoT

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Transaction Fees Minimization in Blockchain-Based Home Delivery System

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IEEE ACCESS 2024年 12卷 109197-109209页

作者： Jandaeng, Chanankorn Chukleang, Tirajet Kongsen, Jonksuk Koad, Peeravit Thu, May Walailak Univ Informat Innovat Ctr Excellence IICE Nakhon Si Thammarat 80160 Thailand Walailak Univ Sch Informat Nakhon Si Thammarat 80160 Thailand Walailak Univ Sch Informat Management Informat Technol Nakhon Si Thammarat 80160 Thailand Cambodia Univ Technol & Sci Fac Engn Phnom Penh 121003 Cambodia

This study investigates the impact of Zlib compression on gas consumption within blockchain systems, focusing particularly on Ethereum transactions. By employing the Ethereum simulator Ganache, we simulate 100 realistic home delivery system datasets to evaluate the performance of compressed versus uncompressed data. The methodology encompasses rigorous statistical analysis to ensure robust results. Our findings reveal that using the Zlib algorithm to compress textual data exceeding 141 bytes before submitting transactions on the Ethereum network reduces the gasUsed while maintaining the system time unchanged. This demonstrates the effectiveness of data compression in optimizing transaction costs without affecting operational efficiency. Additionally, our research extends to analyzing real gasPrice trends on the Ethereum network. We propose a non-linear regression model that accurately predicts hourly gasPrice variations based on the day of the week and the specific time. This provides a valuable tool for users to plan transactions strategically. These insights enhance the understanding of blockchain dynamics and offer practical solutions for improving economic and system efficiency in blockchain operations.

关键词： Blockchains Smart contracts Open source software Cryptocurrency Costs Security compression algorithms Text compression algorithm transaction fees blockchain logistics home delivery

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IPFSz: An Efficient Data compression Scheme in InterPlanetary File System

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IEEE ACCESS 2022年 10卷 122601-122611页

作者： Song, Mansub Han, Jongbeen Eom, Hyeonsang Son, Yongseok Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Chung Ang Univ Dept Comp Sci & Engn Seoul South Korea

InterPlanetary File System (IPFS) is a peer-to-peer (P2P) distributed file system that allows data to be shared and exchanged between connected nodes. Recently, IPFS aims to replace existing protocols used in a centralized system (e.g., HTTP) by overcoming their weaknesses such as a single point of failure and arbitrary control. However, we observe that the performance of IPFS is worse as the data size increases since it demands large numbers of storage and network I/O operations. In this paper, we present an efficient data transmission scheme in IPFS called IPFSz to improve the I/O performance of IPFS. To do this, we extend IPFS by enabling real-time compression functionality while maintaining its existing operations in IPFS. In IPFSz, we compress/decompress the data during I/O operations by using a compression algorithm, manage the states of data (e.g., compressed or decompressed), and provide a compression/decompression interface to applications. Thus, IPFSz can reduce the number of storage and network I/O operations and storage space without sacrificing the integrity of the existing IPFS. We have implemented and evaluated IPFSz on five Amazon EC2 nodes. The experimental results show that IPFSz provides higher performance by up to $7.9\times $ and saves the storage space by up to $6.3\times $ compared with existing IPFS.

关键词： InterPlanetary File System Data compression File systems Servers Metadata compression algorithms Distributed databases InterPlanetary file system data compression decentralized system P2P network distributed file system

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The Study of compression algorithms for WebSocket Protocol

The Study of Compression Algorithms for WebSocket Protocol

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11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： Anusas-amornkul, Tanapat Silawong, Chanapa King Mongkuts Univ Technol North Bangkok Dept Comp & Informat Sci Bangkok 10800 Thailand

ISBN: (纸本)9781479929931

WebSocket protocol is a part of HTML5 standard, which is suitable for real-time and full duplex web applications such as online chat, notification server, and online game systems. A compression algorithm for WebSocket protocol has yet to be defined as a standard. There is a proposal to use Deflate algorithm, a lossless compression algorithm for WebSocket, but other compression algorithms are also available. In this paper, Deflate algorithm is compared with other lossless algorithms, i.e. Huffman, LZ77 and LZW algorithms in order to find a suitable compression algorithm for WebSocket protocol. A compression library is developed using JavaScript for studying and analyzing such compression algorithms for different data formats. In addition, an instant messaging system and an online game are developed and studied as the examples of WebSocket real-time and full duplex web applications. The performance metrics for this study are a compression ratio, compression and decompression speeds and overall time. In our experiments, the deflate compression algorithm gains the best result in terms of overall time and compression ratio for WebSocket protocol.

关键词： component WebSocket protocol compression algorithms Huffman LZ77 LZW and Deflate

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A 16-channel Real-time Adaptive Neural Signal compression Engine in 22nm FDSOI 21

A 16-channel Real-time Adaptive Neural Signal Compression En...

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21st IEEE Interregional NEWCAS Conference (NEWCAS)

作者： Guo, Liyuan Zeinolabedin, Seyed Mohammad Ali Schueffny, Franz Marcus Weisse, Annika Scholze, Stefan George, Richard Partzsch, Johannes Mayr, Christian Tech Univ Dresden Chair Highly Parallel VLSI Syst & Neuromicroelect Dresden Germany

ISBN: (纸本)9798350300246

The real-time multi-channel intracranial recording of neural signals is required in both neuroscientific research and clinical practice. Due to the limited power budget and the increasing number of recording channels, a compression engine is highly recommended. This paper proposes a 16-channel realtime adaptive compression engine (ACE) exploiting neural signal properties. It is able to switch between lossless and near-lossless compression modes. In near-lossless mode, it compresses the spike region lossless and discards the rest. The achieved space-saving ratio (SSR) is on average about 62.5% and 91% for lossless and near-lossless modes, respectively. It can save about 78.5% of the power consumption (near-lossless compression) compared to the transmission of raw neural signals. The 16-channel ACE is implemented in 22nm FDSOI technology and consumes 230.0 mu W dynamic- and 55.49 mu W leakage-power at 5 MHz.

关键词： Multi-channel neural signal compression compression algorithms adaptive coding digital signal processors low-power design

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HLQ: Hardware-Friendly Logarithmic Quantization Aware Training for Power-Efficient Low-Precision CNN Models

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IEEE ACCESS 2024年 12卷 159611-159621页

作者： Choi, Dahun Park, Juntae Kim, Hyun Seoul Natl Univ Sci & Technol Res Ctr Elect & Informat Technol Dept Elect & Informat Engn Seoul 01811 South Korea

With the development of deep learning and graphics processing units (GPUs), various convolutional neural network (CNN)-based computer vision studies have been conducted. Because numerous computations are involved in the inference and training process of CNNs, research on network compression, including quantization, is being actively conducted along with the use of CNNs. Unlike the existing linear quantization, logarithmic quantization has the advantage that the multiply-accumulate (MAC) operation in the convolution (CONV) operation, which occupies most of the CNNs, can be replaced with the addition operation and is suitable for low-precision quantization. In this paper, we propose a logarithmic quantization aware training technique that effectively reduces quantization loss while maximizing the effect of reducing hardware resources and power consumption in the forward and backward propagation processes of the CNN. The proposed method minimizes the accuracy drop by allocating the rounding point with the least quantization loss for each specific training in the forward pass and propagates the optimized gradient by scaling the gradient of parameters with a high quantization loss in the backward pass. As a result of scratch training on the Tiny-ImageNet dataset using ResNet-18, 34, and 50, where both weights and activations are quantized to 4-bits through the proposed method, an improvement in accuracy of 0.88%, 0.48%, and 1.72%, respectively, can be achieved compared to that of the baseline (i.e., full-precision). In addition, as a result of synthesizing the CONV acceleration unit of ResNet-18 through RTL implementation, the proposed 4-bit quantization can achieve a power saving of 82.3% compared to the baseline (i.e., full-precision) when computing ResNet-18.

关键词： Quantization (signal) Accuracy Training Propagation losses Convolutional neural networks Computational modeling Power demand Integrated circuit modeling Hardware Indexes Low power electronics compression algorithms Logarithmic quantization convolutional neural network low-power network compression

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Multi-distribution noise quantisation: an extreme compression scheme for transformer according to parameter distribution

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CONNECTION SCIENCE 2022年第1期34卷 990-1004页

作者： Yu, Zaiyang Li, Shuang Sun, Linjun Liu, Liang Wang Haining Chinese Acad Sci Inst Semicond Beijing Peoples R China Wave Grp Cognit Comp Technol Joint Lab Beijing Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Microelect Beijing 100049 Peoples R China Beijing Key Lab Semicond Neural Network Intellige Beijing 100083 Peoples R China Peoples Publ Secur Univ China Sch Police Adm Beijing Peoples R China

With the development of deep learning, neural networks are widely used in various fields, and the improved model performance also introduces a considerable number of parameters and computations. Model quantisation is a technique that turns floating-point computing into low-specific-point computing, which can effectively reduce model computation strength, parameter size, and memory consumption but often bring a considerable loss of accuracy. This paper mainly addresses the problem where the distribution of parameters is too concentrated during quantisation aware training (QAT). In the QAT process, we use a piecewise function to statistics the parameter distributions and simulate the effect of quantisation noise in each round of training, based on the statistical results. Experimental results show that by quantising the Transformer network, we lose less precision and significantly reduce the storage cost of the model;compared with the full precision LSTM network, our model has higher accuracy under the condition of a similar storage cost. Meanwhile, compared with other quantisation methods on language modelling task, our approach is more accurate. We validated the effectiveness of our policy on the WikiText-103 and PENN Treebank datasets. The experiments show that our method extremely compresses the storage cost and maintains high model performance.

关键词： compression algorithms natural language processing transformer vector quantisation appendices

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HADC: A Hybrid compression Approach for DNA Sequences

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IEEE ACCESS 2022年 10卷 106841-106848页

作者： Elnady, Sarah Sayed, Sabah Salah, Akram Cairo Univ Fac Comp & Artificial Intelligence Comp Sci Dept Cairo 12613 Egypt

In the blossoming age of Next Generation Sequencing (NGS) technologies, genome sequencing has become much easier and more affordable. The large number of enormous genomic sequences obtained demand the availability of huge storage space in order to be kept for analysis. Since the storage cost has become an impediment facing biologists, there is a constant need of software that provides efficient compression of genomic sequences. Most general-purpose compression algorithms do not exploit the inherent redundancies that exist in genomic sequences which is the reason for the success and popularity of reference-based compression algorithms. In this research, a new reference-based lossless compression technique is proposed for deoxyribonucleic acid (DNA) sequences stored in FASTA format which can act as a layer above gzip compression. Several experiments were performed to evaluate this technique and the experimental results show that it is able to obtain promising compression ratios saving up to 99.9% space and reaching a gain of 80% for some plant genomes. The proposed technique also succeeds in performing the compression at acceptable time;even saving more than 50% of the time taken by ERGC in most experiments.

关键词： Bioinformatics Genomics DNA Sequential analysis compression algorithms Encoding Redundancy Bioinformatics DNA sequences reference-based compression greedy alignment

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An Energy-Efficient compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems

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IEEE ACCESS 2022年 10卷 39129-39144页

作者： Fathi, Islam S. Makhlouf, Mohamed Abd Allah Osman, Elsaeed Ahmed, Mohamed Ali Suez Canal Univ Fac Sci Dept Math Ismailia 41522 Egypt Suez Canal Univ Fac Comp & Informat Dept Informat Syst Ismailia 41522 Egypt Nanda Univ Fac Comp Sci Bani Suwayf 62521 Egypt Al Azhar Univ Fac Engn Dept Elect Engn Cairo 11651 Egypt

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

关键词： Electrocardiography Medical services Encoding compression algorithms Data compression Discrete wavelet transforms Sensors Remote healthcare monitoring signal compression electrocardiogram (ECG) energy efficiency krawtchouk moments Ant Lion Optimizer

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A Spectral-Spatial Feature Extraction Method With Polydirectional CNN for Multispectral Image compression

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 2745-2758页

作者： Kong, Fanqiang Hu, Kedi Li, Yunsong Li, Dan Liu, Xin Durrani, Tariq S. Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XQ Lanark Scotland

Convolutional neural networks (CNN) has been widely used in the research of multispectral image compression, but they still face the challenge of extracting spectral feature effectively while preserving spatial feature with integrity. In this article, a novel spectral-spatial feature extraction method is proposed with polydirectional CNN (SSPC) for multispectral image compression. First, the feature extraction network is divided into three parallel modules. The spectral module is employed to obtain spectral features along the spectral direction independently, and simultaneously, with two spatial modules extracting spatial features along two different spatial directions. Then all the features are fused together, followed by downsampling to reduce the size of the feature maps. To control the tradeoff between the rate loss and the distortion, the rate-distortion optimizer is added to the network. In addition, quantization and entropy encoding are applied in turn, converting the data into bit stream. The decoder is structurally symmetric to the encoder, which is convenient for structuring the framework to recover the image. For comparison, SSPC is tested along with JPEG2000 and three-dimensional (3-D) SPIHT on the multispectral datasets of Landsat-8 and WorldView-3 satellites. Besides, to further validate the effectiveness of polydirectional CNN, SSPC is also compared with a normal CNN-based algorithm. The experimental results show that SSPC outperforms other methods at the same bit rates, which demonstrates the validity of the spectral-spatial feature extraction method with polydirectional CNN.

关键词： Feature extraction Image coding Transform coding Convolution Decoding Correlation Convolutional neural networks compression algorithms convolutional neural network (CNN) feature extraction multispectral image rate-distortion optimizer

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