## 1 Introduction  
When monitoring lake water quality, one must deal with a very complex and harsh environment. Wireless sensors are used to meet the demands of real-time online monitoring over large water areas, complex terrains, or remote areas. WSN have been extensively used for environmental monitoring, forest fire prevention, and military applications. A WSN is a self-organized wireless network composed of a large number of sensors. WSN nodes typically use an independent power supply, which means they can be easily deployed in large-scale, complex environments. However, this also means that WSN nodes have very limited energy, memory, and computing resources. It is widely recognized that we must determine effective monitoring and management methods for WSN nodes. Consider the example of lake water quality monitoring using a WSN. This can be a harsh or dangerous environment. In this case, it is typically difficult to change energy sources. Even if we can replace the node's energy supply, the process is expensive. For lake water quality monitoring WSNs, instead of using alternative energy sources, we typically implement effective strategies for reducing the energy consumption and prolonging the life of the network. Suitable management methods for WSNs significantly improve the network performance, reduce energy consumption, and prolong the lifetime of the entire network.  
In this paper, we propose an OWL ontology and SWRL-based knowledge description and semantic reasoning method. This technique is aimed at state monitoring and decision support for WSNs applied to lake water quality monitoring. Our proposed ontology is based on state changes and is an extension of the SSN ontology of SSN-XG and the WSN ontology proposed by [PERSON] _et al._[1, 2]. In our method, the WSN state and corresponding monitoring methods are encoded in the ontology, which is then used to manage the WSN and for self-configuration of the network nodes. Although we do not use the exact same structure as the existing WSN ontology, it does have good compatibility and scalability so that it can be applied in pervasive computing environments. The ontology of the water quality monitoring system manages the WSN according to the nodes' energy, data, networking, observation, and water quality states. To reduce the energy consumption and prolong the life of the network, we have developed a rule set that contains monitoring and management rules developed using SWRL. Managers can select rules according to different monitoring targets. The rule set can be edited and extended, so that different logical rules can be incorporated using a common language. In this paper, we combine the rule set with the ontology to help WSN make intelligent decisions for self-configuration.  
The remainder of this paper is organized as follows: the next section provides an overview of related WSN management systems. In Section 3.1, WSN challenges in a lake water quality monitoring environment are discussed. In Section 3.2, we propose an ontology based on state monitoring and in Section 3.3 we present our SWRL-based reasoning rules for the self-configuration of WSN nodes. Then Section 3.4 describes the workflow of the proposed approach in the system. In Section 4, we describe the evaluation and benchmark experiments, and Section 5 contains our conclusions and directions for future work.