Research On Data Compression Algorithm Based On Orthogonal Curve Fitting In Wireless Sensor Networks

The concept of the Internet of Everything promotes the rapid development of the Internet of Things(IoTs).Wireless sensor networks(WSNs)as key components of the Internet of Things have also achieved significant development.Compared with other wireless communication networks,WSNs do not need to build complex infrastructures.They can perform network configuration and management automatically.Also,they can adjust the network topology dynamically.Due to the deployment environment of a WSN and the characteristics of sensor nodes,the energy problem of the network is a bottleneck that hinders its development.How to reduce energy consumption,increase energy utilization,and extend network lifetime are important topics in WSNs.This paper aims to propose a data compression algorithm that can be applied to a WSN,so as to reduce the energy consumption of data transmission between nodes.It is of great significance to the practical research of WSNs.In this paper,we introduce the structure,characteristics,and applications of WSNs and study data compression algorithms for WSNs.In the network structure,we adopt hierarchical,clustered,and fault-tolerant nodes deployment mode.This mode overcomes the problems of unreliable data transmission and low energy utilization of nodes in traditional deployment method.On this basis,this paper creatively proposed a data compression algorithm based on orthogonal curve fitting.Using orthogonal curve fitting method can avoid system oscillation problem caused by the traditional fitting method and improve the overall stability and practicality of the system.In the aspect of algorithm design,the data transmitter and the data receiver are set with the same parameters and data reconstruction mechanism,which ensures the data accuracy and effectively reduces the amount of data transmission.Finally,we analyze the performance of the algorithm.Also we demonstrate the algorithm in detail with the actual sample data.At the same time,it is compared with other compression algorithms.Simulation experiments are performed with MATLAB.The results show that the algorithm can achieve satisfactory results both in terms of reconstruction accuracy and compression ratio.