Enhancing veracity of IoT generated big data in decision making
Liu, Xiaoli; Tamminen, Satu; Su, Xiang; Siirtola, Pekka; Röning, Juha; Riekki, Jukka; Kiljander, Jussi; Soininen, Juha-Pekka (2018-10-08)
X. Liu et al., "Enhancing Veracity of IoT Generated Big Data in Decision Making," 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Athens, 2018, pp. 149-154, https://doi.org/10.1109/PERCOMW.2018.8480371
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https://urn.fi/URN:NBN:fi-fe2020042822770
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Abstract
Data are crucial to support decision making. If data have low veracity, decisions are not likely to be sound. Internet of Things (IoT) generates big data with inaccuracy, inconsistency, incompleteness, deception, and model approximation. Enhancing data veracity is important to address these challenges. In this article, we summarize the key characteristics and challenges of IoT, which influence data processing and decision making. We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. Moreover, we propose five recommendations for future development of veracious big IoT data analytics that are related to the heterogeneous and distributed nature of IoT data, autonomous decision-making, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management.
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