A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding
- PDF / 3,166,959 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 62 Downloads / 155 Views
(0123456789().,-volV)(0123456789().,-volV)
A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding Chen Chen1 • Limao Zhang1
•
Robert Lee Kong Tiong1
Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Bayesian inference Lossy compression Predictive coding Wireless sensor networks List of CR NSPR eðiÞ e^ðiÞ ½e^ðiÞ xðiÞ ½xðiÞ r ði Þ
symbol The compression ratio The peak-signal-to-noise ratio The residual The predicted residual The approximated predicted residual The signal The approximated signal The reconstruction
& Limao Zhang [email protected] Chen Chen [email protected] Robert Lee Kong Tiong [email protected] 1
School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
qðiÞ J m e
The The The The
quantization index number of samples in one data block width of quantization level error margin
1 Introduction A wireless sensor network (WSN) refers to a group of spatially dispersed and dedicated sensors and actuators [3, 11, 22]. Nodes in a WSN serve different functions. The nodes used for sensing the data is called a sensor node and the one relays the data is called a router.
Data Loading...
