Image compression techniques in wireless sensor networks

Deepthi, S. Aruna; Rao, E. Sreenivasa; Prasad, M. N. Giri

doi:10.1109/icstm.2017.8089170

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Whereas application specific requirements consist of realtime vs. non-real-time, quality of service (QoS)-awareness, and security. Features of these compression techniques include lossless vs lossy, distortion vs accuracy, data aggregation, data correlation, symmetric vs asymmetric, and non-adaptive vs adaptive [8][22][23] [24][25] [26]. Figure 3 is a summary on compression in WSNs according to the types, requirements, and the features.…”

Section: Communication Compressionmentioning

confidence: 99%

Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

et al. 2022

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There is continuous intensive research on image compression techniques in wireless sensor networks (WSNs) in the literature. Some of the image compression techniques in WSNs that exist in the literature include discrete cosine transform (DCT), discrete waveform transforms (DWT), set partitioning in a hierarchical tree (SPIHT), and embedded zero tree wavelet (EZW) coding. Research on image compression in WSNs is necessitated by the need to improve the energy efficiency of sensor nodes and WSNs' lifetimes without compromising the quality of the reconstructed data. Several approaches have been developed centered around image compression and other factors in trying to limit the energy consumption of sensor nodes. Most of these approaches do not provide the error-bound mechanism that balances the rate of compression and distortion of the reconstructed image. Therefore, in this paper, a review and analysis of image compression techniques and approaches in WSNs are conducted. Available image compression approaches in WSNs in literature were then classified according to the image compression technique adopted, and their strengths and weaknesses were highlighted. In addition, a rate-distortion balanced data compression algorithm with error bound mechanism based on artificial neural networks (ANN) in the form of an autoencoder (AE) was coded and simulated in MATLAB before being evaluated and compared to the conventional approaches. The experimental results show that the simulated algorithm has less root mean square error (RMSE) and a higher coefficient of determination (𝑅 2 ) values on variable compression ratios as compared to the Principal Component Analysis (PCA), Discrete Cosine Transform, and Fast Fourier Transform (FFT) when using the Grand-St-Bernard metrological dataset. Furthermore, it presented less RMSE, and higher compression ratio values compared to the Lightweight Temporal Compression (LTC) algorithm on variable error bounds when using the LUCE metrological dataset. Therefore, it was found that the simulated algorithm presents better compression fidelity as compared to the conventional approaches without an error-bound mechanism. Moreover, the algorithm analyzed presents a significant approach to balancing the compression ratio and reconstructed data quality through its error-bound mechanism.

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Section: Communication Compressionmentioning

confidence: 99%

Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

et al. 2022

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“…Aruna Deepthi et al [13] in their paper two algorithms used the block truncation coding (BTC) and Absolute Moment block truncation coding (AMBTC). The results have shown that AMBTC is speeder than BTC in WSN.…”

Section: Figure 2 the Basic Idea Of The Image Compression In Wmsnmentioning

confidence: 99%

Forest Fire Detection Using Wireless Multimedia Sensor Networks and Image Compression

Bouakkaz¹,

Wided²,

Derdour³

2021

I2M

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Recently, the issue of multimedia sensors received considerable critical attention, that led to the apparition of Wireless Multimedia Sensor Networks (WMSNs) WMSN that different from wireless sensor networks (WSN) by using multimedia sensors that can process video, audio, image data besides scalar data and send it to station base (SB). Multimedia data have a big volume bigger than scalar data and need more resources and consumed more energy. The ideal solution to solve the problems of WMSN (big volume, energy consumption) is data compression. Forest plays a critical role in our daily life we can summarize the importance of forests in human life. Among the most dangerous events the forest fires that happen because of natural or Man-made. Many methods used to detect forest fires the newest are: wireless multimedia sensor networks. Our system of detecting forest fire has been developed using a wireless multimedia senor network with two types of sensors (scalar, images). In the first phase when the scalar sensors detected a high temperature its announced alarm to activate the image sensors. In the second phase for detecting fire the image sensors, we used image processing tools. When the zone of fire in the image captured was detected the phase of compression started using the down sampling method. the final phase is transmission data to the station base using the grid chain transmission protocol technique, which allows a critical optimization of energy consumption. So, maximizing network life. The competence of the proposed system is achieved by minimizing size of image transmitted with grid chain routing protocol.

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“…The experimental results show that proposed algorithm is effective and secure. The authors [7] have used discrete cosine transforms with Hadamard transforms and haar transform for image compression and transmission enhancement in wireless sensors networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of compression algorithms for information security: A Review

Sharma

Batra

2018

ICST Transactions on Scalable Information Systems

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Data compression is a vital part of information security, since compressed data is much more secure and convenient to handle. Effective data compression technique creates an effective, secure, easy communicable & redundant data. There are two types of compression algorithmic techniques:-lossy and lossless. These techniques can be applied to any data format like text, audio, video or image file. The primary objective of this study was to analyse data compression techniques used for information security techniques like steganography, cryptography etc. Four each, lossy and lossless techniques are implemented and evaluated on parameters like-file size, saving percentage, time, compression ratio and speed. Detailed analysis shows that the lossy techniques performs better quantitatively whereas lossless is better qualitatively. However, lossless techniques are more effective as there is no data loss during the process. Among all, Huffman encoding outperforms other algorithms.

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Image compression techniques in wireless sensor networks

Cited by 6 publications

References 2 publications

Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

Forest Fire Detection Using Wireless Multimedia Sensor Networks and Image Compression

Performance analysis of compression algorithms for information security: A Review

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