Sensor networks can be viewed as resources constrained distributed database systems, of which a significant challenge is to develop reliable, energy-efficient methods to extract useful information from distributed sensor data. Most of the existing event (region) detection approaches rely on using raw sensory data, which results in a large amount of data transmission as well as is time-consuming. However, it is difficult to ensure accurate results due to the imprecision and uncertainty of the raw sensor data. In many cases, users neither care about these raw sensory data nor pay attention to the data format during in-network filtering or fusion, but want to get natural language-like semantic event information, such as “how serious it is”, “is it credible?” Moreover, the main technique of the existing event detection is neighboring cooperation, which requires great data exchange between neighboring nodes. It is costly in terms of energy and time. This paper proposes a novel fuzzy methodology based semantic event region query processing approach. Semantic event information instead of raw sensor data is used for in-network fusion, and fuzzy method based distributed semantic event information description, filtering and fusion approaches are devised. The experimental evaluation based on real data set show that the proposed approach has good performance in terms of energy efficiency and reliability.