In Internet of Things (IoT) ubiquitous environments, a high volume of heterogeneous data is produced from different devices in a quick span of time. In all IoT applications, the quality of information plays an important role in decision making. Data fusion is one of the current research trends in this arena that is considered in this paper. We particularly consider typical IoT scenarios where the sources measurements highly conflict, which makes intuitive fusions prone to wrong and misleading results. This paper proposes a taxonomy of decision fusion methods that rely on the theory of belief. It proposes a data fusion method for the Internet of Things (DFIOT) based on Dempster–Shafer (D–S) theory and an adaptive weighted fusion algorithm. It considers the reliability of each device in the network and the conflicts between devices when fusing data. This is while considering the information lifetime, the distance separating sensors and entities, and reducing computation. The proposed method uses a combination of rules based on the Basic Probability Assignment (BPA) to represent uncertain information or to quantify the similarity between two bodies of evidence. To investigate the effectiveness of the proposed method in comparison with D–S, Murphy, Deng and Yuan, a comprehensive analysis is provided using both benchmark data simulation and real dataset from a smart building testbed. Results show that DFIOT outperforms all the above mentioned methods in terms of reliability, accuracy and conflict management. The accuracy of the system reached up to $$99.18\%$$ 99.18 % on benchmark artificial datasets and $$98.87\%$$ 98.87 % on real datasets with a conflict of $$0.58 \%$$ 0.58 % . We also examine the impact of this improvement from the application perspective (energy saving), and the results show a gain of up to $$90\%$$ 90 % when using DFIOT.

中文翻译：

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DFIOT：物联网数据融合

在物联网 (IoT) 无处不在的环境中，不同设备会在很短的时间内产生大量异构数据。在所有物联网应用中，信息质量在决策中起着重要作用。数据融合是本文考虑的该领域当前的研究趋势之一。我们特别考虑了来源测量高度冲突的典型物联网场景，这使得直观的融合容易产生错误和误导性的结果。本文提出了一种基于信念理论的决策融合方法分类法。它提出了一种基于 Dempster-Shafer (D-S) 理论和自适应加权融合算法的物联网数据融合方法 (DFIOT)。它在融合数据时考虑了网络中每个设备的可靠性和设备之间的冲突。这是在考虑信息寿命、分离传感器和实体的距离以及减少计算的同时。所提出的方法使用基于基本概率分配 (BPA) 的规则组合来表示不确定信息或量化两个证据主体之间的相似性。为了与 D-S、Murphy、Deng 和 Yuan 进行比较，研究所提出方法的有效性，使用基准数据模拟和来自智能建筑试验台的真实数据集进行了综合分析。结果表明，DFIOT 在可靠性、准确性和冲突管理方面优于上述所有方法。该系统在基准人工数据集上的准确率高达 $99.18\%$$ 99.18 %，在真实数据集上达到 $98.87\%$$ 98.87 %，冲突为 $0.58 \%$$ 0.58 %。