Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring
提出一种基于多传感器数据流的实时现象识别机制，通过共识理论融合上下文信息、时间序列预测聚合值，并利用二型模糊推理系统在不确定性下提高识别准确率，减少误报。
Data stream monitoring provides the basis for building intelligent context-aware applications over contextual data streams. A number of wireless sensors could be spread in a specific area and monitor contextual parameters for identifying various phenomena, e.g., fire or flood. A back-end system receives measurements and derives decisions for possible abnormalities related to negative effects. We propose a mechanism which, based on multivariate sensors data streams, provides real-time identification of phenomena. The proposed framework performs contextual information fusion over consensus theory for the efficient measurements aggregation while time-series prediction is adopted to result future insights on the aggregated values. The unanimous fused and predicted pieces of context are fed into a type-2 fuzzy inference system to derive highly accurate identification of events. The type-2 inference process offers reasoning capabilities under the uncertainty of the phenomena identification. We provide comprehensive experimental evaluation over real contextual data and report on the advantages and disadvantages of the proposed mechanism. Our mechanism is further compared with type-1 fuzzy inference and other mechanisms to demonstrate its false alarms minimization capability.