High-quality talent cultivating Internet of Things (IoT) Engineering is the basis for the rapid development of IoT technology. To train high-quality application-oriented IoT technical talents, guided by educational psychology, this article conducts in-depth research; analyzes the characteristics of IoT Engineering; makes professional talent cultivating programs and cyclical adjustment plans; builds a high-quality teaching system based on the professional knowledge system of the IoT; explores the “spiritual level” and “psychological level” characteristics of teachers and students in teaching; highly integrates “Industry-University-Research-Competition” from the perspective of students, teachers, and colleges; infiltrates positive psychological cues appropriately; formulates the construction method of the “student teaching assistant” auxiliary system to enhance the efforts to promote learning by learning; and finally innovates the talent cultivating system for the IoT Engineering. The implementation results show that the students trained by this system have a solid foundation of knowledge related to IoT Engineering and strong engineering practice application, adaptability, and innovation ability.

The covering problem and node deployment is a fundamental problem in the research of wireless sensor network. The number of nodes and the coverage of a network can directly affect performance and operating costs. For this reason, we propose an EMAC algorithm (Energy Efficient Multi-target Associate Coverage Algorithm). The algorithm uses the correlation between the nodes and dynamic grouping to adjust coverage area. Within the coverage area, we use the greedy algorithm to optimize the coverage area. So, the target node is uniformly covered by other sensor nodes and the network resources is optimized. To ensure the energy balance in wireless sensor network and extend the lifetime of it, we only wake partial nodes to work in a cycle, so that, they can work in turn. Experiments show that the algorithm can effectively reduce energy consumption, with better adaptability and effectiveness.

The density and depth of grain pile directly affect the evaluation of silo storage. However, there is no report on the detection of mean density of grain pile. To make up for this gap, this paper establishes the relationship between density and dielectric constant through free space transmission method, offering a feasible way to detect the mean density of grain pile. Considering the popularity of ground penetration radar (GPR) in silo detection, the author derived the formula of maximum detection depth, based on the special environment of the silo, the detection quality factor of the radar, and the features of the scattering cross-section. After that, the proposed method was applied to compute the density and depth of grain piles in actual silos. Finally, our method was proved to be accurate through simulation and experiments. The research findings shed new light on the electromagnetic detection of the density and depth of grain pile in actual silos.