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
Enhancing veracity of IoT generated big data in decision making
|Author:||Liu, Xiaoli1; Tamminen, Satu1; Su, Xiang1;|
1Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
2VTT Technical Research Centre of Finland Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042822770
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-04-28
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.
|Pages:||149 - 154|
2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
IEEE International Conference on Pervasive Computing and Communications Workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research has been partly funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 646428). Dr. Xiang Su would like to thank Jorma Ollila Grant of Nokia foundation for funding his research.
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