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The Key Technologies of Distributed Compressed Sensing in Wireless Sensor Networks

Author: PanTaiPeng
Tutor: HuHaiFeng
School: Nanjing University of Posts and Telecommunications
Course: Communication and Information System
Keywords: Wireless sensor networks Distributed compressed sensing Joint sparse model Spatial correlation Space-time correlation
CLC: TN929.5
Type: Master's thesis
Year: 2012
Downloads: 263
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Abstract


Wireless sensor network (WSN, Wireless Sensor Networks) has a large number of nodes, nodes easily limited resources as well as the composition isomorphism characteristics. Therefore, how sensing data data compression, and energy efficient way to meet the application requirements of WSN WSN application problems to be solved through the use of WSN nodes. In recent years, a new kind of signal compression coding theory - compressed sensing (CS, Compressed Sensing) gradually developed. Compressed sensing has low complexity encoding compression the performance independent encoding and decoding, so the CS particularly suitable for application in a resource-constrained WSN. WSN node perception data has a spatial and temporal correlation, distributed compressed sensing (DCS, Distributed Compressed Sensing) It is focused on the research through the use of signal within the correlation and cross correlation of multiple signal reconstruction, therefore, DCS in WSN has broad application prospects. Firstly, on the status and application of wireless sensor networks are introduced in detail, a more profound exposition and discussion of the basic theoretical framework of compressed sensing, sparse specifically including signal conversion, signal measurement and signal compression refactoring and other key technology for a detailed description. Then, according to the wireless sensor network node perceptual spatial correlation of the data, and the establishment of a model of spatial correlation of distributed wireless sensor networks based on compressed sensing, encoding and decoding algorithms are proposed on the basis of this model, and studied the distributed compressed sensing reconstruction error and the relationship between the data compression ratio. On this basis, to study the temporal and spatial the WSN nodes awareness data wireless sensor networks and distributed compressed sensing joint sparse model established joint sparse model-based space-time correlation distributed compressed sensing model proposed codec program, and the distributed compressed sensing joint reconstruction algorithm and the independent reconstruction algorithm to carry out a detailed comparison. Finally, through the simulation of this established model and algorithm validation. The simulation results show that we created based JSM-2 joint sparse model spatial and temporal correlation distributed compression-aware algorithm can significantly reduce the encoding side data compression ratio, which means that the model has a higher storage efficiency. Therefore, we can conclude: wireless sensor networks based on the joint sparse model of space-time correlation of distributed compression algorithm perceptual model reconstruction performance and compression ratio has certain advantages for practical applications in wireless sensor networks better feasibility.

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