Research On Data Compression In Wireless Sensor Networks

WSN has many characteristics,such as the large scale,the high distribution density and the random sow in some applications.There are redundant information between nodes and the data collected by nodes usually,if all the data is directly transmitted to the sink node without any processing,there will be a lot of redundant information in the sink node.The nodes waste the energy and even the transmission process maybe exist the bandwidth conflict.However,the power energy,communication capacity and computation capacity of sensor nodes are all limited.So how to effectively compress information within the nodes,remove redundant data between nodes,reduce network traffic and improve the utilization of resources are impo,rtant research contents.Data compression technology can effectively solve the bottleneck of limited energy.This paper uses Compressed Sensing(CS)algorithm.CS is a method of saving the energy,which is totally different from traditional compression algorithm.The algorithm intially collectes and compresses data at the same time,so the data collected is compressed data,then the compression data is transfered.In the end we reconstruct the original data according to the compression conditions.In Wireless Sensor Networks,the transmission data consumes far more energy than calculation,so we can know the CS algorithm is superior to traditional compression algorithm.The paper mainly researches the reconstruction algorithm of Compressed Sensing including several parts as follows:1.An Improved Iterative Hard Thresholding(IHT)algorithm is proposed.In the known Compression Sensing reconstruction algorithms,some can satisfy the reconstruction accuracy very well,but algorithm is inconvenient to be applied to the practical problems because of its complexity and slow reconstruction speed.Some algorithms are simple and easy to operate,the data loss is too large without good reconstruction precision.Improved IHT algorithm considers both practicality and reconstruction time,which is easy to operate and guarantees the accuracy.2.Compressed sensing technology is applied in the wireless sensor network.In actual applications,as sensor nodes and data are numerous,data redundancy exists between nodes.According to the compression algorithm of a single sensor node,the paper combining with the joint sparse model,can effectively solve the issue of sensory data correlation.