Deep imputation network for missing-value imputation of concurrent continuous missing patterns using relationship among multiple IoT data streams in a smart space = 다중 사물인터넷 데이터 스트림의 관계성을 이용한 연속 누락 패턴 값 대체에 관한 연구

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Missing values with various missing patterns and the high missing rates from many different data streams of Internet of Things (IoT) devices remain challenging problems. In this regard, while analyzing a dataset collected from a smart space with multiple IoT devices, we found a continuous missing pattern that is quite different from the existing missing-value patterns. A pattern has large blocks of continuous missing values of over a few seconds and up to a few hours in length. In addition, concurrent continuous missing patterns occurring simultaneously in multiple IoT data streams are very common. Given the explosive growth of IoT data streams, such a pattern is a vital factor regarding the availability and reliability of IoT applications; however, it cannot be solved using existing missing-value imputation methods. Therefore, a novel approach for the missing-value imputation of concurrent continuous missing patterns is required in a smart space with multiple IoT data streams. We deliberated that, even if the missing values of a continuous missing pattern occur in multiple IoT data streams, imputation of these missing values is possible through the learning of other related data streams using a machine-learning method. To solve the missing values of a continuous missing pattern, we analyzed multiple IoT data streams in single smart space and determined their relationship such as their correlation and causality, where the correlation is a linearly proportionate inter-dependency, and causality indicates the cause and effect among multiple IoT data streams in a smart space. To substitute the missing values of concurrent continuous missing patterns, we propose a deep-learning based missing-value imputation model exploiting information on various relationships, i.e., a deep imputation network (DeepIN), in an IoT environment. DeepIN uses multiple long short-term memories that are constructed according to the relationship of each IoT data stream. We evaluated DeepIN on the IoT dataset from our real smart-office testbed, and the results of our experiments showed reasonable accuracy (75$\sim$80\%) of missing value imputation when continuous missing patterns occur simultaneously in up to three IoT data streams. Furthermore, our proposed approach dramatically improves the imputation performance over the state-of-the-art missing-value imputation algorithm. Based on our extensive experiments and analyses, we found that the causality information among multiple IoT data streams is the most significant factor in the missing-value imputation of concurrent continuous missing patterns. Based on these results, our proposed approach can be a promising methodology that enables many smart space services and applications, even if a long-term block of values is missing in IoT environments.
Hyun, Soonjooresearcher현순주researcherLee, Youngheeresearcher이영희researcher
한국과학기술원 :전산학부,
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학위논문(박사) - 한국과학기술원 : 전산학부, 2019.2,[v, 113 p. :]


Smart space▼amissing-value Imputation▼acontinuous missing pattern▼amultiple IoT data streams▼arelationship▼adeep learning; 스마트 공간▼a누락 값 대체▼a연속누락패턴▼a복합 스마트 데이터 스트림▼a관계성▼a딥러닝

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