Practical data compression in wireless sensor networks: A survey

Srisooksai, Tossaporn; Keamarungsi, Kamol; Lamsrichan, Poonlap; Araki, Kiyomichi

doi:10.1016/j.jnca.2011.03.001

Cited by 227 publications

(153 citation statements)

References 85 publications

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“…As shown in [14] and references therein, residues of different real-world data (temperature, humidity, solar radiation, etc.) fit well with zero-mean Gaussian or Laplace distributions.…”

Section: Related Workmentioning

confidence: 99%

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

Campobello

Segreto

Zanafi

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper we propose a new lossless compression algorithm suitable for Internet of Things (IoT). The proposed algorithm, named RAKE, is based only on elementary counting operations and has low memory requirements, and therefore it can be easily implemented in low-cost and low-speed microcontrollers as those used in IoT devices. Despite its simplicity, simulation results show that, in the case of sparse sequences, the proposed algorithm outperforms well-known lossless compression algorithms such as rar, gzip and bzip2. Moreover, in the case of real-world data, RAKE achieves higher compression ratios as even compared to IoT-specific lossless compression algorithms.

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“…As shown in [14] and references therein, residues of different real-world data (temperature, humidity, solar radiation, etc.) fit well with zero-mean Gaussian or Laplace distributions.…”

Section: Related Workmentioning

confidence: 99%

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

Campobello

Segreto

Zanafi

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

show abstract

“…We identify a wide variety of coding schemes in the literature (e.g., [3], [13], [14]) and discuss some important solutions for signal compression in WSNs in the following.…”

Section: Related Workmentioning

confidence: 99%

“…However, designing a compression algorithm for multimodal data is much more challenging than the single modal situation [13]. To study fundamental issues and design tradeoffs, we ignore the case of multimodal data in this paper and keep it for a future work.…”

Section: Energy Conservation By Data Compressionmentioning

confidence: 99%

Rate-Distortion Balanced Data Compression for Wireless Sensor Networks

et al. 2016

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Abstract-This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world datasets and compared with conventional methods for temporal and spatial data compression. The experimental validation reveals that the proposed algorithm outperforms several existing WSN data compression methods in terms of compression efficiency and signal reconstruction. Moreover, an energy analysis shows that compressing the data can reduce the energy expenditure, and hence expand the service lifespan by several folds.Index Terms-Lossy data compression, error bound guarantee, compressing neural networks, Internet of things.

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“…Leveraging the spatial-temporal properties in sensory data from real deployments, in-network compression is an essential technique to reduce the amount of data transmission while preserving relatively high reconstruction accuracy in the sink [6,7,8]. The emergence of compressive sensing (CS) theory has opened up a new research avenue for in-network compression.…”

Section: Introductionmentioning

confidence: 99%

Research on Data Fusion Technology Based on Compressed Sensing

Tian

2016

Int. J. Onl. Eng.

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Abstract-First introduce a traditional compressed sensing data fusion technique is introduced, and with the goal of addressing the shortcoming of the traditional method, a compressed sensing data fusion technology based on temporal-spatial correlation is proposed. Then modeling of the proposed data fusion technique, improves upon the CS reconstruction algorithm and presents the iterative threshold reconstruction algorithm based on temporal-spatial correlation. With the synthetic signals as the research objects, experiments show the effectiveness of the algorithm, further proving that it displays obvious improvement for the proposed data fusion technology to reduce the network load and saves the amount of energy consumption of the network.

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Practical data compression in wireless sensor networks: A survey

Cited by 227 publications

References 85 publications

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

Rate-Distortion Balanced Data Compression for Wireless Sensor Networks

Research on Data Fusion Technology Based on Compressed Sensing

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