With the rapid development of scientific and technological revolution, the Internet of Thing systems have been widely deployed in many vertical domains such as smart cities, smart grids, smart agriculture, smart medical healthcare, etc. In these scenarios, it is an urgent problem to provide a high-effect IoT solution to support high computation ability with strong data security and privacy constraints. In this paper, we propose a novel secure data computing platform based on blockchain crowdsourcing computation. Each data sample is transmitted to its private blockchain for secure data storage. To support a high computation ability, we present a powerful edge network constituted of nearby edge gateways, edge devices, etc. Thus, these data on the blockchain can be partitioned and allocated to these edge nodes in a crowdsourcing way. We also deploy different AI tools to meet the requirements of vertical applications. Finally, we evaluate our system performance with a real crowdsourced blockchain computation platform to show our effectiveness in vertical application, e.g., face detection.

A surface slicing planning method based on the K-means clustering algorithm and improved fruit fly optimization algorithm (FOA) is proposed to address the efficiency problem in the surface processing of special shaped workpieces. The NURBS surface reconstruction is performed on the complex surface of the selected workpiece, and the K-means clustering algorithm with the curvature-distance factor is used to subdivide the surface, and the FOA with the hopping strategy is used to plan the optimal connection path for each subdivision with different starting points. This improves the local search capability of the FOA by improving the mixing of different classes that occurs when the surface is subdivided. Finally, the proposed method is demonstrated to be effective for surface slicing by simulating the slicing plan on the constructed heterogeneous workpiece surface, and the shortest connectivity paths are obtained for different starting points.