Data Visualization in Internet of Things: Tools, Methodologies, and Challenges

被引:35
作者
Protopsaltis, Antonis [1 ]
Sarigiannidis, Panagiotis [1 ]
Margounakis, Dimitrios [2 ]
Lytos, Anastasios [2 ]
机构
[1] Univ Western Macedonia, Dept Elect & Comp Engn, Kozani, Greece
[2] Sidroco Holdings Ltd, Limassol, Cyprus
来源
15TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY, ARES 2020 | 2020年
关键词
Internet of Things (IoT); Data Visualization; Big Data; Anomaly Detection; OF-THE-ART; BIG-DATA; VISUAL ANALYTICS; ANOMALY DETECTION; PLATFORM; OPPORTUNITIES; FRAMEWORK; INDUSTRY; ISSUES;
D O I
10.1145/3407023.3409228
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:11
相关论文
共 93 条
[1]   Graph based anomaly detection and description: a survey [J].
Akoglu, Leman ;
Tong, Hanghang ;
Koutra, Danai .
DATA MINING AND KNOWLEDGE DISCOVERY, 2015, 29 (03) :626-688
[2]  
Ali SM, 2016, PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING AND INFORMATICS (IC3I), P656, DOI 10.1109/IC3I.2016.7918044
[3]  
[Anonymous], 2014, P 5 INT C FUT EN SYS
[4]   Industry 4.0 as Digitalization over the Entire Product Lifecycle: Opportunities in the Automotive Domain [J].
Armengaud, Eric ;
Sams, Christoph ;
von Falck, Georg ;
List, Georg ;
Kreiner, Christian ;
Riel, Andreas .
SYSTEMS, SOFTWARE AND SERVICES PROCESS IMPROVEMENT (EUROSPI 2017), 2017, 748 :334-351
[5]  
Baheti R., 2011, IMPACT CONTROL TECHN, V12, P161, DOI DOI 10.1145/1795194.1795205
[6]   Building an IoT Data Hub with Elasticsearch, Logstash and Kibana [J].
Bajer, Marcin .
2017 5TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (FICLOUDW) 2017, 2017, :63-68
[7]  
Baranwal T, 2016, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), P597, DOI 10.1109/CONFLUENCE.2016.7508189
[8]  
Bastian M., 2009, Proceedings of the International AAAI Conference on Web and Social Media, V3, P361, DOI DOI 10.1609/ICWSM.V3I1.13937
[9]  
Batista AFM, 2016, IEEE INT CONF MOB, P349, DOI [10.1109/MASS.2016.26, 10.1109/MASS.2016.052]
[10]   Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions [J].
Bhattarai, Bishnu P. ;
Paudyal, Sumit ;
Luo, Yusheng ;
Mohanpurkar, Manish ;
Cheung, Kwok ;
Tonkoski, Reinaldo ;
Hovsapian, Rob ;
Myers, Kurt S. ;
Zhang, Rui ;
Zhao, Power ;
Manic, Milos ;
Zhang, Song ;
Zhang, Xiaping .
IET SMART GRID, 2019, 2 (02) :141-154