The stability degree of key nodes is an important indicator of wireless sensor network performance. Appropriate node importance evaluation method is a precondition for the identification of key node and the analysis on network stability. The current methods based on average length and network density are unable to make real-time evaluation on nodes in practical application. Thus, this paper puts forward a node importance evaluation method in wireless sensor network based on energy field model. Based on the data exchange at the network layer in wireless sensor network, this approach analyzes the node properties and correlation among nodes with energy field and graph theory, which shows the influence result of key nodes in wireless sensor network. Therefore, the node influence can be worked out so as to evaluate the importance of node in wireless sensor network. The experimental results show that, compared with the current node importance evaluation methods of wireless sensor network, the approach proposed in this paper not only is able to evaluate the node importance without affecting the operation of network but also shows the dynamic change in node importance.
Edge detection is a vital part in image segmentation. In this paper, a novel method based on adjacent dispersion for edge detection is proposed. This method utilizes adjacent dispersion to detect edges, avoiding thresholds selection, anisotropy in convolution computation and discontinuity in edges, and it is composed of two modules, namely the dispersion operator and the refinement. The dispersion is to obtain a matrix of discrete coefficient of a gray level image and the refinement is to thin edges to one-pixel-point and ensure it logically continuous. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors, Canny and Sobel. Experiment results indicate that the proposed method performs well without thresholds and offers superior performance in continuity in edge detection in digital images.
The catering industry is a humungous service-based industry, which includes food nutrition and security, transporting, tourism, and similar services. Employing the new techniques, such as machine learning and deep learning, competing firms can improve their strategies and deliver better services with lower price.In this work, we investigate a large number of historical dining data that reflect customer spending habits and taste preferences through terminal devices at the edge of the network, which can be used for customers to recommend dishes when ordering. With the popularity of cloud computing today, it is also prone to some shortcomings when the amount of computing is too large, such as poor real-time performance, high server pressure, data security risks, and so on. Especially, the leakage of user privacy often has incalculable consequences. Based on this, this article proposes a hot pot dish recommendation algorithm based on data mining and food nutrition for edge devices, which can effectively protect user privacy. The experimental data in this article is derived from the massive consumption data of customers in China's large hot pot enterprises. First, clean the raw data and mask the user privacy to get feature data unrelated with user privacy that can be used for dish recommendation. On the basis of this, combined with data mining methods, diet, and other indicators, the results of final recommended dish can respond to both user preferences and nutrition arrange.The experimental results show that this dish-recommended method is more reasonable and healthier.
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