Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks.

In the traditional visual simultaneous localization and mapping (SLAM), the strong static assumption leads to a large degradation in the accuracy of visual SLAM in dynamic environments. For this reason, many scholars incorporate semantic segmentation networks into the visual SLAM framework to extract dynamic information in images. However, most semantic segmentation networks consume a lot of computing time due to the large model size, which leads to the algorithm's inability to meet real-time requirements in practical applications. In this paper, a real-time visual SLAM algorithm based on deep learning is proposed. This novel algorithm is based on ORB-SLAM2, and a parallel semantic thread based on the lightweight object detection network YOLOv5s is designed, which enables us to get semantic information in the scene more quickly. In the tracking thread, an optimized homography matrix module is proposed, which utilizes semantic information to optimize and solve the homography matrix so that we can calculate a more accurate optical flow vector. In the optical flow module, the semantic information is used to narrow down the calculation range of the optical flow value to improve the real-time performance of the system, and the dynamic feature points in the image are removed by the optical flow mask to improve the accuracy of the system. Experimental results show that compared with ORB-SLAM2, DynaSLAM, and other excellent visual SLAM algorithms, this algorithm can effectively reduce the absolute trajectory error of visual SLAM in dynamic environments. Compared with other deep learning-based visual SLAM algorithms, the real-time performance of this algorithm is also significantly improved.INDEX TERMS SLAM, dynamic environment, semantic, optical flow method, pose estimation.

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Research on smart homes (SHs) has increased significantly in recent years because of the convenience provided by having an assisted living environment. The functions of SHs as mentioned in previous studies, particularly safety services, are seldom discussed or mentioned. Thus, this study proposes a semantic approach with decision support for safety service in SH management. The focus of this contribution is to explore a context awareness and reasoning approach for risk recognition in SH that enables the proper decision support for flexible safety service provision. The framework of SH based on a wireless sensor network is described from the perspective of neighbourhood management. This approach is based on the integration of semantic knowledge in which a reasoner can make decisions about risk recognition and safety service. We present a management ontology for a SH and relevant monitoring contextual information, which considers its suitability in a pervasive computing environment and is service-oriented. We also propose a rule-based reasoning method to provide decision support through reasoning techniques and context-awareness. A system prototype is developed to evaluate the feasibility, time response and extendibility of the approach. The evaluation of our approach shows that it is more effective in daily risk event recognition. The decisions for service provision are shown to be accurate.