改进压缩感知算法的WSN数据恢复方法  

Data recovery method using improved compression sensing algorithm in WSN

作　　者：陈雪 胡玉平 CHEN Xue;HU Yu-ping(Department of Computer Science and Engineering,Guangzhou College of Technology and Business,Guangzhou 510850,China;Information Science School,Guangdong University of Finance and Economics,Guangzhou 510320,China)

机构地区：[1]广州工商学院计算机科学与工程系,广东广州510850 [2]广东财经大学信息学院,广东广州510320

出　　处：《计算机工程与设计》2020年第5期1219-1226,共8页Computer Engineering and Design

基　　金：广东省自然科学基金项目(2016A030313717);2018年教育部高等教育司产学合作协同育人基金项目(201801324005)。

摘　　要：针对WSN数据恢复成本比例较高的问题,提出一种利用改进压缩感知算法和单位圆盘图模型的WSN数据恢复方法。利用改进压缩感知算法恢复部分丢失数据的节点;将这些已恢复的节点数据当作已知,联合原有的正常节点,基于不同的网络拓扑,使用数据骡子进行剩余丢失数据的恢复;在改进压缩感知算法的支撑下,通过二次规划实现数据重构,采用一组具有先进移动能力的移动传感器来访问失效传感器的邻居节点,重新获取丢失数据。利用NS2仿真软件进行实验,仿真结果表明,相比其它几种较新算法,提出算法完成数据恢复所用成本更低。Aiming at problem of the high cost of data recovery in wireless sensor networks,a data recovery schemes based on improved compression sensing algorithm and unit disk graph model in wireless sensor networks was proposed.The improved compression sensing algorithm was used to recover some of the nodes that data lost,and these recovered node data were treated as known,combined with the original normal nodes,the remaining lost data were recovered using data mules based on different network topologies.Under the support of the improved compression sensing algorithm,the data were deconstructed by quadratic programming and a set of mobile sensors with advanced mobility capabilities was used to re-acquire lost data by visiting the neighbors of failed sensors.The NS2 simulation software was used to carry out the experiment.The simulation results show that the cost of data recovery is lower than several other compared algorithms.

关 键 词：改进压缩感知 最优汇聚树 无线传感器网络 单位圆盘图模型 数据恢复 NS2仿真软件 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]