Fawzy Dina, Moussa Sherin, Badr Nagwa
Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt.
Sensors (Basel). 2021 Oct 23;21(21):7035. doi: 10.3390/s21217035.
Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in-data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.
物联网（IoT）中针对各种应用生成了海量异构传感数据。这些大数据具有与物联网相关的附加特征，包括可信度、时间和空间特征。这揭示了在处理过程中需要考虑的更多视角，给不同融合级别用于收集和分析的传统数据融合方法带来了巨大挑战。本文提出了一种基于物联网的时空数据融合（STDF）方法，用于低级别数据的数据输入输出融合，以实现实时空间物联网源聚合。它通过利用基于大数据分析的传统数据融合方法实现了最佳性能，同时除了数据量和速度外，还专门保留了数据过期、可信度以及物联网数据的时空视角。在从所有物联网源采集数据时，它应用聚类采样进行数据约简。对于每个源，它利用k均值聚类进行空间分析和Tiny AGgregation（TAG）进行时间聚合相结合的方式，在处理服务器上维持时空数据融合。STDF通过一个公共物联网数据流模拟器进行了验证。实验检验了不同数据集中各种物联网处理挑战，将数据大小减少了95%，处理时间减少了80%，对于所使用的最大数据集，准确率高达90%。
