摘要：
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 timeconsuming.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 languagelike 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.%传感器网络可以看成是一个资源受限的无线分布式数据库系统,如何设计低功耗高可靠的数据处理方法,从分布式的感知数据中获取用户感兴趣的信息是一个挑战性工作.现有的事件(区域)检测方法大都基于原始的感知数据,处理大规模的原始感知数据的通信和时间开销很大,然而这些原始数据由于本身的不精确性和不确定性,难以保证得到精确的处理结果.大多数情况,用户并不关心这些原始感知数据或者网内过滤/融合时的数据形态,而是想得到类似自然语言的“有多严重？”、“可信吗？”等语义事件信息.此外,现有的事件区域检测方法主要是利用邻居协作来提高检测的准确性,而邻居协作需要大规模的网内数据交换,非常耗时耗能.鉴于上述问题,提出一种新的基于模糊方法的语义事件区域查询处理方法,语义事件信息代替原始的感知数据用于网内过滤和融合,并设计了基于模糊方法的分布式语义事件信息表示、过滤和融合算法.基于真实数据集的仿真实验表明了该方法在兼顾节能和可靠性方面有良好的表现.
