知识图谱特征重构下无线传感网络数据存储恢复

Data Storage and Recovery in Wireless Sensor Networks Based on Knowledge Graph Feature Reconstruction

为了提升无线传感网络数据存储恢复成功率,保障无线传感网络数据存储安全性,提出知识图谱特征重构下无线传感网络数据存储恢复方法。通过基于知识图谱的特征重构方法优化数据存储结构;利用BP神经网络和LEACH算法,对特征重构后的数据进行融合。最后,结合多级网络编码和纠删码的原理,构建多级编码矩阵对融合数据进行多级编码,并生成多份数据副本进行存储,实现无线传感网络数据存储恢复。实验结果表明,该方法能够提升数据存储恢复成功率至90%以上,通信代价低于1.5×10^(5) Mbit/s,存储恢复时间低于0.7 ms,可以在提升恢复成功率的同时,降低存储通信代价和存储恢复时间。

To improve the success rate of wireless sensor network data storage recovery and ensure the security of wireless sensor network data storage,a knowledge graph feature reconstruction method for wireless sensor network data storage recovery is proposed.Firstly,the feature reconstruction method based on knowledge graph reconstructs the data of wireless sensor networks and expands the data storage structure.Secondly,the BP neural network and LEACH algorithm are used to fuse the data after feature reconstruction to improve the reliability and stability of the data.Finally,based on the principles of multi-level network coding and erasure codes,a multilevel coding matrix is constructed to encode the fused data and generate multiple copies of the data for storage.When data are damaged,recovery of damaged data is achieved through data encoding and direct calculation of data blocks,ensuring the integrity and reliability of wireless sensor network data.The experimental results show that the proposed method can improve the success rate of data storage recovery to over 90%,and the communication cost is less than 1.5×10^(5) Mbit/s,with a storage recovery time of less than 0.7 ms,improving the recovery success rate while reducing storage communication costs and storage recovery time.