【标题】Multi-path reliable routing technology of industrial internet of things based on sparrow search algorithm
【参考中译】基于麻雀搜索算法的工业物联网多路径可靠路由技术
【类型】 期刊
【关键词】 Sparrow search algorithm; Industrial internet of things; Multipath routing; Reliable routing; Internet of things; IoT
【参考中译】 麻雀搜索算法；工业物联网；多路径路由；可靠路由；物联网
【作者】 Chungeng Ma; Wenjing Ma; Lixia Hou
【摘要】 The routing technology of industrial internet of things (IoT) has no obvious advantages in reliability and balance, so a multi-path reliable routing technology of industrial IoT based on sparrow search algorithm is proposed in this paper. Firstly, a topology model is constructed by comprehensively considering the node part and path connection mode of the industrial IoT. Then the hierarchical protocol is used to set up the routing protocol, and the trust degree of nodes and paths of the industrial IoT are calculated. Finally, the sparrow search algorithm is used to select the reliable data transmission path. Through route switching and updating, the multi-path reliable routing technology of the industrial IoT is realised. The experimental results show that the data delivery rate of this technology is increased by 2.67%, and the routing load balance is significantly improved, that is, it has obvious advantages in reliability and balance.
【参考中译】 针对工业物联网（IoT）路由技术在可靠性和均衡性方面没有明显优势的问题，提出了一种基于麻雀搜索算法的工业物联网多路径可靠路由技术。首先，综合考虑工业物联网的节点部分和路径连接方式，构建了拓扑模型。然后采用分层协议建立路由协议，计算工业物联网节点和路径的信任度。最后，利用麻雀搜索算法选择可靠的数据传输路径，通过路由切换和更新，实现了工业物联网的多路径可靠路由技术。实验结果表明，该技术的数据传递率提高了2.67%，路由负载均衡性得到了显著改善，即在可靠性和均衡性方面具有明显优势。
【来源】 International Journal of Internet Manufacturing and Services 2023, vol.9, no.4
【入库时间】 2024/3/28
【标题】Data fusion method of industrial internet of things based on fuzzy theory
【参考中译】基于模糊理论的工业物联网数据融合方法
【关键词】 Clustering routing protocol; Fuzzy theory; Fuzzy classification; Membership function
【参考中译】 分簇路由协议；模糊理论；模糊分类；隶属函数
【作者】 Qiaoyun Chen; Chunmeng Lu
【摘要】 In order to overcome the problem of poor data fusion effect of data fusion method, this paper proposes a data fusion method of industrial internet of things based on fuzzy theory. Firstly, the data acquisition area is divided and the data is collected by the absolute median difference method. Secondly, fuzzy set is constructed to extract data attribute features according to membership function. Then, the trusted data is screened by clustering routing protocol and classified by exponential smoothing method. Finally, the spatial and temporal correlation degree is used to allocate the fusion weights, and the industrial internet of things data fusion is carried out by fuzzy theory. Experimental results show that the classification accuracy of the proposed method can reach 99%, the data fusion rate can reach 99.5%, and the fusion time is only 3.92 s. The proposed method can improve the data fusion effect.
【参考中译】 为了克服数据融合方法数据融合效果差的问题，提出了一种基于模糊理论的工业物联网数据融合方法。首先划分数据采集区域，采用绝对中值差分法采集数据。其次，根据隶属函数构造模糊集，提取数据属性特征。然后，采用分簇路由协议对可信数据进行筛选，并采用指数平滑法对可信数据进行分类。最后，利用空间和时间相关度分配融合权值，利用模糊理论对工业物联网数据进行融合。实验结果表明，该方法的分类准确率可达99%，数据融合率可达99. 5%，融合时间仅为3. 92 s该方法可以提高数据融合效果。
【标题】Information data perception allocation method for industrial internet of things based on dissimilarity algorithm
【参考中译】基于相异性算法的工业物联网信息数据感知分配方法
【关键词】 Improved ant colony algorithm; Data distribution delay function; Dissimilarity algorithm; Data aware allocation; Initial cluster centre
【参考中译】 改进蚁群算法；数据分布延迟函数；相异度算法；数据感知分配；初始聚类中心
【作者】 Yinlei Tian; Yan Yang
【摘要】 In order to improve the efficiency and accuracy of industrial IOT information data perception and allocation, this paper proposes an industrial IOT information data perception and allocation method based on dissimilarity algorithm. Firstly, the dissimilarity matrix is constructed to determine the data attribute values. Secondly, according to the method of dissimilarity degree, the candidate point set of clustering centre point is determined. Then, generation allocation matrix is built to calculate the storage capacity of the node ID, and the security of the data node is calculated through the dissimilarity algorithm. Finally, the poor security allocation scheme is removed, and the information and data perception allocation results of the internet of things are obtained. The experimental results show that the distribution recall rate of this method can reach 97.2%, the distribution accuracy is 97.3%, and the distribution time is only 5.6 s, indicating that this method can improve the data aware distribution effect.
【参考中译】 为了提高工业物联网信息数据感知和分配的效率和准确性，提出了一种基于相异算法的工业物联网信息数据感知和分配方法。首先，构造相异矩阵来确定数据属性值。其次，根据相异度方法确定聚类中心点的候选点集。然后，建立发电分配矩阵计算节点ID的存储容量，并通过相异度算法计算数据节点的安全性。最后，去掉安全性差的分配方案，得到物联网的信息和数据感知分配结果。实验结果表明，该方法的分发召回率可达97.2%，分发正确率为97.3%，分发时间仅为5.6 S，表明该方法能够提高数据感知分发效果。