无线传感器网络中具有容错能力的上下文捕获多值决策融合
Context-Capture Multi-Valued Decision Fusion With Fault Tolerant Capability For Wireless Sensor Networks
摘要：
无线传感器网络（WSNs）通常用于执行事件检测的决策融合。目前的决策融合方案都是基于二值决策的，没有考虑突发上下文捕获。然而，突发上下文和多值数据是无线传感器网络的重要特征。一方面，来自传感器的本地决策通常具有突发性和上下文特征。融合中心必须捕获来自传感器的突发上下文信息。另一方面，在实践中，许多应用需要处理多值数据，例如用于闪电预测的温度和反射水平。为了解决这些挑战，马尔可夫调制泊松过程（MMPP）和多值逻辑被引入到无线传感器网络中执行上下文捕获多值决策融合。整个决策融合分解为两个部分。第一部分是基于叠加MMPP的无线传感器网络上下文捕获模型。通过这个过程，融合中心有更高的概率得到有用的本地决策的传感器。第二个是多值决策融合。也可以基于MVL执行故障检测。一旦融合中心检测到故障节点，则从似然比的计算中移除其所有局部决策。最后，我们评估了上下文捕获和容错的能力。结果支持我们的计划的有用性。
英文摘要：
Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in practice, many applications need to process multi-valued data, such as temperature and reflection level used for lightening prediction. To address these challenges, the Markov modulated Poisson process (MMPP) and multi-valued logic are introduced into WSNs to perform context-capture multi-valued decision fusion. The overall decision fusion is decomposed into two parts. The first part is the context-capture model for WSNs using superposition MMPP. Through this procedure, the fusion center has a higher probability to get useful local decisions from sensors. The second one is focused on multi-valued decision fusion. Fault detection can also be performed based on MVL. Once the fusion center detects the faulty nodes, all their local decisions are removed from the computation of the likelihood ratios. Finally, we evaluate the capability of context-capture and fault tolerant. The result supports the usefulness of our scheme.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
