大规模物联网生态系统的分布式决策融合

Ashwin Raut, Divesh Kumar, V. Chaurasiya, Manish Kumar
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引用次数: 0

摘要

物联网数据分析有许多应用程序,这些应用程序产生大量数据,以获得新的见解和信息。然而,由于物联网数据源的异质性、不必要的数据处理、决策的不确定性、数据偏差以及不断增加的数据大小,这项工作仍然具有挑战性。为了克服这些挑战,我们提出了大规模物联网生态系统的分布式决策融合框架。拟议的框架分为三个层次。第一级和第二级采用基于滤波方法的特征选择和动态分类器选择准则进行决策,提供小个体生态系统的局部决策;而第三层则使用多数投票、加权多数投票和分布式朴素贝叶斯分类器来融合从小生态系统收集的决策。最后,我们展示了所提出的解决方案在US-Accidents数据集上的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Decision Fusion for Large Scale IoT- Ecosystem
IoT data analytics have numerous applications that generate huge data to gain new insights and information. How-ever, this work remains challenging due to the heterogeneity of IoT data sources, unnecessary data processing, uncertainty in decision-making, data biasness, and ever-increasing data size. To overcome these challenges, we propose distributed decision fusion framework for the large-scale IoT ecosystem. The proposed framework has divided into three-level. The first and second level provides the local decision of the small individual ecosystem using the filter method-based feature selection and dynamic classifier selection criteria for decision making; whereas the third level fuses the collected decision from the small ecosystems using Majority voting, Weighted majority voting and distributed Naive Bayes classifier. Lastly, we illustrate performance of the proposed solution on the US-Accidents dataset.
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