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Cacophony: Building a resilient Internet of things

机译：cacophony：建立一个有弹性的东西互联网

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The proliferation of sensors in the world has created increased opportunities for context-aware applications. However, it is often cumbersome to capitalize on these opportunities due to the difficulties inherent in collecting, fusing, and reasoning with data from a heterogeneous set of distributed sensors. The fabric that connects sensors lacks resilience and fault tolerance in the face of infrastructure intermittency. To address these difficulties, we introduce Cacophony, a network of peer-to-peer nodes (CNodes), where each node provides real-time predictions of a specified set of sensor data. The predictions from each of the Cacophony prediction nodes can be used by any application with access to the Web. Creating a new CNode involves three steps: (1) Developers and domain-knowledge experts, via a simple Web UI, specify which sensor data they care about. Possible sources of sensor data include stationary sensors, mobile sensors, and the real-time Web; (2) The CNode automatically aggregates data from the relevant sensors in real time using a JXTA-based peer-to-peer network; and, (3) The CNode uses the aggregated data to train a prediction model via the Weka machine-learning library (Hall, et al. , 2009). Real-time predictions made by the CNode are then made publicly available to applications that wish to use data from a CNode’s particular set of sensors. The real-time predictions themselves can also be used recursively as sensor data, enabling the creation of CNodes that make predictions based on other CNodes.
机译：世界上传感器的激增为上下文知识应用程序创造了增加的机会。然而，由于收集，融合和推理来自来自异构的分布式传感器的数据所固有的困难，它往往很麻烦。连接传感器的织物在面对基础设施间歇性中缺乏弹性和容错。为了解决这些困难，我们介绍了CACOCOCOCONONE，一个对等节点（CNODE）的网络，其中每个节点提供指定的传感器数据集的实时预测。可以通过访问网络的任何应用程序使用来自每个Cacophony预测节点的预测。创建新CNODE涉及三个步骤：（1）开发人员和域知识专家通过简单的Web UI，指定了他们关心的传感器数据。可能的传感器数据来源包括固定式传感器，移动传感器和实时网; （2）CNODE使用基于JXTA的对等网络实时从相关传感器自动聚合数据;并且，（3）CNODE使用聚合数据通过Weka机器学习库（Hall，等，2009）培训预测模型。然后，CNODE的实时预测将公开可用于希望使用来自CNODE的特定传感器集的数据的应用程序。实时预测本身也可以递归方式作为传感器数据来使用，从而能够创建基于其他CNodes进行预测的CNODE。

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