Perceptual Big Data Hierarchical Perception Compression Coding Method

Abstract

The invention discloses a sensing big data hierarchical sensing compressed coding method. The sensing big data hierarchical sensing compressed coding method comprises steps that a sensing node is usedfor compressing and sampling of original data, and is used to transmit the data to a cluster head node; the cluster head node is used for the compressing and coding of the compressed and sampled datareceived from a lower layer and the data received from other cluster head nodes, and is used to transmit the data to a processing node by using a probability p; the processing node is used to code the data received from the lower layer and the data received from other processing nodes, and is used to transmit the coded data to an aggregation node by using the probability p; the aggregation node is used for decoding operation, and approximate original data is acquired. By combining with the network coding, the compressing sensing, and the hierarchical sensing theories, the space-time correlation of the sensing big data is fully excavated, especially the space-time correlation of the inter-cluster data is fully excavated, and therefore multi-resolution data service is provided for user of different types, and at the same time, an ideal hypothesis of sharing an observation matrix in advance is eliminated.

Description

technical field [0001] The invention belongs to the field of wireless communication networks, and in particular relates to a perceptual big data hierarchical perceptual compression coding method. Background technique [0002] With the continuous development of smart earth and smart city, the continuous deployment of wireless sensing devices has brought the perception data into a new era of explosive growth. As one of the main sources of a large amount of sensory data, Wireless Sensor Network (WSN) is a distributed network capable of sensing and monitoring the physical world, and has been widely used in intelligent transportation, environmental monitoring, industrial production, medical and health , military and other fields. Obviously, the traditional WSN can no longer meet the processing and management requirements of a large amount of sensory data. Therefore, how to overcome this challenge has become a research focus. Intuitively, the mismatch between the growth rate of...

AI Technical Summary

Problems solved by technology

[0005] The second type of scheme studies compression coding from the perspective of data space-time correlation. A major feature of this scheme is that it considers the time and space correlation of data at the same time, but it integrates the two into a whole, resulting in a high reconstruction process. Computational complexity

[0006] The third type of scheme also considers the time and space correlation of data at the same time, but does not integrate the two into a whole, but considers them relatively staggered. This mode can reduce the calculation without reducing the accuracy of data recovery. and communication overhead, and the implementation is relatively simple, but when the perception data of the spatial dimension fluctuates greatly, the data reconstruction error of this scheme will be large

[0007] The fourth type of scheme designs a low-complexity clustering space-time compression coding mechanism by integrating NC, CS and clustering ideas, and obtains better data reconstruction performance, but there is still room for improvement in terms of compression gain and reconstruction accuracy

[0008] In summary, the current research program has problems such as insufficient mining of the spatial-temporal correlation of perceptual big data, low quality of data recovery, and inability to provide multi-resolution data services.

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