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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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nie, Wang, Liu and Yang.)

When the nodes in the network are deployed in the target area with an appropriate density, the effective aggregation and transmission of the data gathered in the monitoring area remain to be solved. The existing Compressed Sensing (CS) based on data aggregation schemes are accomplished in a centralized manner and the Sink node achieves the task of data aggregation. However, these existing schemes may suffer from load imbalance and coverage void issues. In order to address these problems, we propose a Compressed Sensing based on Fault-tolerant Correcting Data Aggregation (CS-FCDA) scheme to accurately reconstruct the compressed data. Therefore, the network communication overhead can be greatly reduced while maintaining the quality of the reconstructed data. Meanwhile, we adopt the node clustering mechanism to optimize and balance the network load. It is shown via simulation results, compared with other data aggregation schemes, that the proposed scheme shows obvious improvement in terms of the Fault-tolerant correcting capability and the network energy efficiency of the data reconstruction., Competing Interests: All authors declare no conflict of interest.