基于时间相关性的无线传感器网络数据压缩与优化算法

针对无线传感器网络(WSN)数据采集存在数据冗余度大、累积误差大和数据精度低等问题，根据采集数据之间的时间相关性，提出一种无线传感器网络数据压缩与优化算法。该算法通过分析时间序列中采集数据的线性关系，建立分段一元线性回归模型；根据采集数据与回归模型预测值之间的误差，自适应地调整下一个采集时间，并动态地优化回归模型。仿真结果表明该算法在不同的数据变化情况下，均能降低数据冗余度和网络通信量，提高采集数据的重构精度。最后在真实的无线传感器网络应用环境中验证了算法的可行性。

Data compression and optimization algorithm for wireless sensor network based on temporal correlation

Concerning to the problem that Wireless Sensor Network (WSN) data collection has large data redundancy, large cumulative error and low data accuracy, according to the temporal correlation between collection data, a data compression and optimization algorithm for WSN was proposed. It established segmented one-dimensional linear regression model by analyzing linear relationship of collection data in temporal series. According to the error between collection data and predicted value of regression model, it adaptively adjusted next collection time, and dynamically adjusted the regression model. The simulation results show that the proposed algorithm can reduce the data redundancy and network traffic, and improve the reconfiguration precision of the collection data under different conditions of data changes. The test results in a real scenario show the feasibility of the proposed algorithm.