基于稀疏图码的物联网邻居节点发现  

Neighbor node discovery of IoT based on sparse graph codes

作　　者：周华乔 徐义晗 孙一凡 曾维军 Zhou Huaqiao;Xu Yihan;Sun Yifan;Zeng Weijun(College of Computer&Communication,Jiangsu Vocational College of Electronics&Information,Huai’an Jiangsu 223001,China)

机构地区：[1]江苏电子信息职业学院计算机与通信学院,江苏淮安223001

出　　处：《计算机应用研究》2022年第6期1829-1833,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61802425);淮安市科技项目(HAP201904)

摘　　要：为了解决物联网中发现新节点的传统蜂窝随机接入方案不能适用于大规模节点的传感器网络的问题,首先基于组测试框架将邻居发现问题转换为压缩感知理论模型中的单向量测量问题,然后对测量矩阵进行精心构造,最后提出一种新颖的基于稀疏图码理论的逐步剥离恢复算法来解决物联网邻居节点发现问题。实验结果表明,该算法在低样本和时间复杂度下显著提高了大规模无线传感器网络活动邻居节点发现的有效性和准确性。In order to solve the problem that the traditional cellular random access scheme for discovering new nodes in the Internet of Things is not suitable for the sensor network of large-scale nodes,this paper firstly transformed the neighbor discovery problem into a single vector measurement problem in the compressed sensing theoretical model based on the group testing framework,and then constructed the measurement matrix carefully.Finally,it proposed a novel stepwise stripping recovery algorithm based on sparse graph code theory to solve the neighbor node discovery problem in the Internet of Things.The experimental results show that the proposed algorithm improves the effectiveness and accuracy of active neighbor node discovery in large-scale wireless sensor networks with low sample and time complexity.

关 键 词：无线传感器网络 组测试 压缩感知 邻居发现 稀疏图码 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]