@conference{Protopsaltis2020,
title = {Data Visualization in Internet of Things: Tools, Methodologies, and Challenges},
author = { A. Protopsaltis and P. Sarigiannidis and D. Margounakis and A. Lytos},
url = {https://www.researchgate.net/publication/343935293_Data_Visualization_in_Internet_of_Things_Tools_Methodologies_and_Challenges},
doi = {10.1145/3407023.3409228},
isbn = {9781450388337},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the 15th International Conference on Availability, Reliability and Security},
publisher = {Association for Computing Machinery},
address = {Virtual Event, Ireland},
series = {ARES '20},
abstract = {As the Internet of Things (IoT) grows rapidly, huge amounts of wireless sensor networks emerged monitoring a wide range of infrastructure, in various domains such as healthcare, energy, transportation, smart city, building automation, agriculture, and industry producing continuously streamlines of data. Big Data technologies play a significant role within IoT processes, as visual analytics tools, generating valuable knowledge in real-time in order to support critical decision making. This paper provides a comprehensive survey of visualization methods, tools, and techniques for the IoT. We position data visualization inside the visual analytics process by reviewing the visual analytics pipeline. We provide a study of various chart types available for data visualization and analyze rules for employing each one of them, taking into account the special conditions of the particular use case. We further examine some of the most promising visualization tools. Since each IoT domain is isolated in terms of Big Data approaches, we investigate visualization issues in each domain. Additionally, we review visualization methods oriented to anomaly detection. Finally, we provide an overview of the major challenges in IoT visualizations.},
keywords = {Anomaly Detection, Big Data, Data visualization, Internet of Things (IoT)},
pubstate = {published},
tppubtype = {conference}
}
