Exploring the Internet of Things sequence-structure detection and supertask network generation of temporal-spatial-based graph convolutional neural network

The study is designed to improve the efficiency of Internet of Things (IoT) structure detection and achieve the smooth operation of IoT networks. First, the connection between the IoT network structure and maxdegree is investigated based on analyzing the IoT supertask network structure to find the main influence factor of maxdegree, along with the conditions for obtaining the optimal maxdegree. Second, a structural algorithm model of optimal supertask network is proposed as the foundation for achieving the minimum maxdegree. Finally, the human behavior recognition database is taken as the research object to verify its performance through the specific instance data. The IoT network structure factors are proved to include the task quantity, resource capacity, number of networks, and Communication Calculation Ratio (CCR). The experimental results also show that the principal factor that affects maxdegree is the number of different tasks. Besides, there is a mutually positive interaction between the network structure and the IoT maxdegree, which complement each other and form the core network of IoT. Moreover, the results reflect the good performance on different datasets of the supertask IoT network structure for the human behavior recognition database. There exists the optimal maxdegree of the model under the condition of 40 tasks, 32 resources, 6 networks and CCR of 6. Furthermore, the proposed algorithm has a shorter length and lower complexity than other related algorithms, which is very suitable for the construction of IoT networks. The research results can provide some references and practical value for the construction and data processing of the IoT structure.