Abstract
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.
Highlights
The Internet of Things (IoT) is an emerging technology that connects various objects in the physical world in order to communicate and exchange data [1,2]
We propose the spatiotemporal data fusion (STDF) approach for IoT data; To the best of our knowledge, STDF is the first data in–data out (DAI–DAO) data fusion approach for IoT data that is independent of any IoT domain; To the best of our knowledge, STDF is the first data fusion approach for IoT data that preserves the spatial and temporal characteristics of IoT data during fusion, considering all timing characteristics of IoT data; STDF uniquely investigates predefined and trusted IoT data sources to ensure private
Each dataset is identified by its IoT domain that clarifies the IoT application, data size in gigabytes (GB), time span in seconds (s), features that indicate the nature of the dataset attributes, the modality, the specific considered IoT data dimensions that are involved in the dataset and the evaluation metric applied to the dataset, being either the processing time (PT) or accuracy level (AL)
Summary
The Internet of Things (IoT) is an emerging technology that connects various objects in the physical world in order to communicate and exchange data [1,2] It plays a vital role in different practical systems for decision support and control by providing intelligent services and applications as a major source of big data [3,4]. The scope of this study focuses on low-level data fusion: data in–data out fusion directly from IoT sources This provides ready-fused data streams, which can be considered for further intended business purposes and domain-specific applications to obtain a domain-specific data out, feature out or decision out.
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