An adaptive Huffman algorithm for data compression in wireless sensor networks

Săcăleanu, Dragoş Ioan; Stoian, Rodica; Ofrim, Dragos Mihai

doi:10.1109/isscs.2011.5978764

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…The compressions based on the dictionary technique are now implementing new quaternary methods instead of traditional binary codes [7]. At present, Huffman coding is efficiently running on data mining [8] and wireless sensor networks to compress data [9], [10]. Some word-based compression exploits each word considering a basic unit [4].…”

Section: Introductionmentioning

confidence: 99%

Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly

Sarker¹,

Rahman²

2022

IJCDS

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This research works on high symbolic Bengali text and transforms it into corresponding less symbolic English complying with the transliteration method. The Huffman-based approaches serve to compress retaining the original quality of the data. On the other hand, faster encoding and decoding is the most sophisticated sphere in data compression. We propose an adjacent distance array, a novel data structure based on the Huffman principle for encoding and decoding the character of transliterated text. The encoding and decoding algorithms have been explained for the introduced modus operandi and juxtaposed with conventional Huffman-based algorithms. Our research is outdoing than any regular Huffman-based algorithms, concentrating on the speed of the encoding and decoding manner discovered after estimating all decisions.

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Section: Introductionmentioning

confidence: 99%

Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly

Sarker¹,

Rahman²

2022

IJCDS

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“…The dictionary-based compression methods are Ziv-Lempel (Fenwick, 1996), substituting the first occurrence of a string for the appearance of another exact string (Lelewer and Hirschberg, 1987). Today, Huffman coding approaches perform smooth data compression in data mining (Oswald et al, 2015) and wireless sensor networks (Săcăleanu et al, 2011;Renugadevi and Darisini, 2013) area. Recently, the short message service (SMS) texting and internet-formed short text messages (such as Messenger, WhatsApp, and Twitter) have sparked a surge in demand for short text message compression.…”

Section: Introductionmentioning

confidence: 99%

A Huffman based short message service compression technique using adjacent distance array

Sarker¹,

Rahman²

2022

IJICT

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The short message service (SMS) is a wireless medium of transmission that allows you to send brief text messages. Cell phone devices have an uttermost SMS capacity of 1,120 bits in the traditional system. Moreover, the conventional SMS employs seven bits for each character, allowing the highest 160 characters for an SMS text message to be transmitted. This research demonstrated that an SMS message could contain more than 200 characters by representing around five bits each, introducing a data structure, namely, adjacent distance array (ADA) using the Huffman principle. Allowing the concept of lossless data compression technique, the proposed method of the research generates character's codeword utilising the standard Huffman. However, the ADA encodes the message by putting the ASCII value distances of all characters, and decoding performs by avoiding the whole Huffman tree traverse, which is the pivotal contribution of the research to develop an effective SMS compression technique for personal digital assistants (PDAs). The encoding and decoding processes have been discussed and contrasted with the conventional SMS text message system, where our proposed ADA technique performs outstandingly better from every aspect discovered after evaluating all outcomes.

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“…As the transfer progresses, the new node will be added to the tree and the tree will be reconfigured. Reference [8] proposes an adaptive dictionary-based dynamic algorithm. The algorithm utilizes temporal data correlation and Huffman compression.…”

Section: Introductionmentioning

confidence: 99%

CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks

Liu

Chen

Yan

et al. 2019

Sensors

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Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.

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An adaptive Huffman algorithm for data compression in wireless sensor networks

Cited by 7 publications

References 5 publications

Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly

Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly

A Huffman based short message service compression technique using adjacent distance array

CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks

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