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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Predictive intelligence to the edge through approximate collaborative context reasoning
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Predictive intelligence to the edge through approximate collaborative context reasoning

机译：通过近似协作背景推理预测到边缘的预测智能

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摘要

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference.
机译：我们专注于物联网（物联网）的环境，其中传感和计算设备网络负责本地过程上下文数据，原因和协作推断特定现象的外观（事件）。将处理和知识推断推动到物联网网络的边缘，允许将事件推理过程的复杂性分布到许多可管理的部件中，并且要物理地位于上下文信息的源。这使得能够在传统的集中云系统上实时处理大量的丰富数据流。我们在每个物联网设备（传感器/执行器）上的不确定度下提出了一种轻量级，节能，分布，自适应，多语境的透视事件推理模型。每个设备感应和处理基于不同本地上下文的观点的上下文数据和Infers事件：（i）关于事件表示的专家知识，（ii）异常值推断和（iii）与本地预测的上下文的偏差。通过上下文化的协作信念驱动的聚类过程实现了这种新颖的近似推理范式，其中根据他们对事件存在的信念形成装置的集群。我们的分布式和联合智能模型通过汇总当地的信仰，更新和调整其知识，有效地在上下文数据上有效地识别上下文数据上的任何本地化异常。我们通过其他局部和集中的事件检测模型提供了对我们模型的全面的实验和比较评估，通过其他局部和集中的事件检测模型，并通过实现最多三个数量的能耗和高质量的推理来显示源于采用的益处。

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