Context-adaptive quotient and remainder encoding method used for sensing data of wireless sensing nodes

Abstract

The invention discloses a context- adaptive quotient and remainder encoding method used for sensing data of wireless sensing nodes. The method comprises the following steps of orderly calculating differences of N sensing data to be encoded to obtain original difference values; respectively converting the original difference values to obtain the difference data, wherein the initial data digit D is 1, and i is equal to 1; using 2D as a divisor to obtain a quotient Q and a remainder R when the present difference data is used as a dividend; carrying out fixed-length encoding to the quotient, and carrying out length-variable encoding to the remainder; enabling the data digit D to be equal to the digit of the present difference data; and judging whether the next uncoded different data exists or not, if so, i is equal to i plus one, and executing the step 3, and if not, accomplishing the encoding to obtain the quotient and remainder codes of the difference data. The context- adaptive quotient and remainder encoding method used for sensing data of wireless sensing node has high compression ratio on both slowly changed data and non-slowly changed data, and can be smoothly operated among sensing nodes to realize lossless data compression. The context-adaptive quotient and remainder encoding algorithm used for sensing data of wireless sensing nodes has the advantages of low operation complexity and small occupied storage space.

Description

technical field [0001] The invention belongs to the technical field of data compression, and in particular relates to a context adaptive quotient encoding method for sensing data of wireless sensor nodes. Background technique [0002] The application of wireless sensor network (WSN) is more and more extensive. However, the energy consumption of wireless sensor nodes is one of the bottlenecks restricting its application. Studies have shown that the energy of sensor nodes is mainly consumed in the process of wireless data transmission, so the research of various data compression algorithms has quickly become a new hot spot in wireless sensor networks. [0003] At present, there have been many studies on lossy data compression in sensor networks, but there are only a few algorithms for lossless data compression based on sensor nodes. In 2006, Sadler C.M. and Martonosi M. proposed a dictionary-based lossless data compression algorithm called S-LZW algorithm, which is an improve...

AI Technical Summary

Problems solved by technology

[0011] Although lossy compression techniques can significantly improve the compression ratio, it is often at the expense of discarding a large amount of original data and / or losing the accuracy of the original data

The main application of wireless sensor networks is to monitor various environmental parameters, and many parameters need to maintain their original accuracy, and lossy compression algorithms cannot meet this requirement

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