A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding

Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression...

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Vydáno v:Wireless networks Ročník 26; číslo 8; s. 5981 - 5995
Hlavní autoři: Chen, Chen, Zhang, Limao, Tiong, Robert Lee Kong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2020
Springer Nature B.V
Témata:
Accuracy
Algorithms
Bayesian analysis
Coding
Communications Engineering
Compression ratio
Computer Communication Networks
Computing costs
Data compression
Data storage
Data transmission
Digital media
Electrical Engineering
Engineering
IT in Business
Missing data
Networks
Predictions
Regression models
Sensors
Statistical analysis
Statistical inference
Wireless networks
Wireless sensor networks
Lossy compression
Predictive coding
Wireless sensor networks
Bayesian inference
ISSN:1022-0038, 1572-8196
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Popis
Shrnutí:Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.
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ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-020-02425-w