Performance evaluation of arithmetic coding data compression for internet of things applications

Khairi, Nor Asilah; Jambek, Asral Bahari; Ismail, Rizalafande Che

doi:10.11591/ijeecs.v13.i2.pp591-597

Cited by 8 publications

(9 citation statements)

References 5 publications

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“…One of the main entropy coding types creates and assigns every single symbol of the entry into a unique prefix-free code. There are more than 16 algorithms support entropy algorithms such as Arithmetic Coding [ 71 , 72 , 73 , 74 ], Asymmetric Numeral Systems (ANS) [ 75 , 76 , 77 ], Golomb Coding [ 78 , 79 ], Adaptive Huffman [ 80 , 81 , 82 ], Canonical Huffman [ 83 ], Modified Huffman [ 84 ], Range encoding [ 85 , 86 ], Shannon [ 87 ], Shannon–Fano [ 88 , 89 , 90 ], Shannon–Fano–Elias [ 91 ], Tunstall coding [ 92 , 93 ], Unary coding [ 94 , 95 , 96 ], Universal Exp-Golomb [ 97 , 98 ], Universal Fibonacci Coding [ 99 , 100 , 101 ], Universal Gamma Coding [ 102 , 103 ], Universal Levenshtein Coding [ 104 ].…”

Section: Compressionmentioning

confidence: 99%

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Nasif

Othman

Sani

2021

Sensors

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Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Huffman based on deep learning concepts using weights, pruning, and pooling in the neural network. The proposed algorithm is believed to obtain a better compression ratio. Additionally, in this paper, we also discuss the challenges of applying the proposed algorithm to IoT data compression due to the limitations of IoT memory and IoT processor, which later it can be implemented in IoT networks.

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Section: Compressionmentioning

confidence: 99%

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Nasif

Othman

Sani

2021

Sensors

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“…The individual model exhibits its own pros and cons and is best fit for specific types of target complications. A discussion of problems in the Internet of Things (IoT) application [5][6][7][8][9] using regression analysis has been presented in [10]. In addition, each model can be recyclable and is built by a training set of algorithms using classical data.…”

Section: Related Studiesmentioning

confidence: 99%

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Jittawiriyanukoon¹

2019

IJEECS

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<span>Multiple Regression-Based Prediction (MRBP) is an emerging calculation to or analysis technique cope with the future by compiling the history of data. The MRBP characteristic will include an approximation for the associations between physical observations and predictions. MRBP is a predictive model, which will be an important source of knowledge in terms of an interesting trend to be followed in the future. However, there is impairment in the MRBP dataset, wherein each form of missing and noisy data has caused an error and is unavailable further analysis. To overcome this unavailability, so that the data analytics can be moved on, two treatment approaches are introduced. First, the given dataset is denoised; next, listwise deletion (LD) is proposed to handle the missing data. The performance of the proposed technique will be investigated by dealing with datasets that cannot be executed. Employing the Massive Online Analysis (MOA) software, the proposed model is investigated, and the results are summarized. Performance metrics, such as mean squared error (MSE), correlation coefficient (COEF), mean absolute error (MAE), root mean squared error (RMSE), and the average error percentage, are used to validate the proposed mechanism. The proposed LD projection is confirmed through actual values. The proposed LD outperforms other treatments as it only requires less state space, which reflects low computation cost, and proves its capability to overcome the limitation of analysis.</span>

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“…Recently, due to the extremely rapid increase in the amount of information transmitted, computer networksare overloaded due to the low bandwidth of existing communication channels. This problem can be solved in two ways: by replacing existing communication lines with new ones with greater bandwidth, or by introducing new methods of data compression [1][2][3][4][5][6][7]. The first method requires significant financial costs, in addition, it is not always possible to replace communication lines; more often it is more advisable to use existing lines for data transmission than to lay new ones.…”

Section: Introductionmentioning

confidence: 99%

Increasing the efficiency of information transmission in communication channels

Zhurakovskyi

Boiko

Druzhynin

et al. 2020

IJEECS

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<span lang="EN-US">This paper discusses compression methods focused on data transmission over communication channels. The characteristics of different algorithms for different types of incoming data are analyzed. The purpose of this study is to evaluate the speed of operation of each of the compression algorithms for different types of information and different compression parameters, on the basis of the obtained results to make recommendations for the application of compression methods in systems critical to the performance of the algorithm. Based on the results of the analysis, the methods of compression that can be used in communication channels are selected: LZW, LZH, Vitter and matrix. The practical research of the selected methods on different information flows (text, graphics, measurement data, combined data) was carried out, their comparative analysis was performed. Research has highlighted compression methods that give the most optimal results in each case. Comparative evaluation of algorithms for different parameters is made, the possibility of data compression implementation in systems running in real time is analyzed. Based on the results of the study, recommendations are made for the application of particular compression methods in specific conditions.</span>

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Performance evaluation of arithmetic coding data compression for internet of things applications

Cited by 8 publications

References 5 publications

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Increasing the efficiency of information transmission in communication channels

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