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Wireless sensor networks constitute the platform of a broad range of applications related to national security, surveillance, military, health care, and environmental monitoring. The coverage of WSN has answered the questions about quality of service (surveillance) which can be provided by WSN. Therefore, maximizing coverage using the resource constrained nodes is a non-trivial problem. The coverage problem for wireless sensor network (WSN) has been studied extensively in recent years, especially when combined with connectivity and energy efficiency. In this paper we present a survey of coverage problem. And besides some basic design considerations in coverage of WSN we describe two challenges, namely, maximizing network lifetime and network connectivity. We also provide a brief summary and comparison of existing coverage schemes. 

Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

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