ABSTRACT
In IoT applications, data from various sensors are processed and delivered in different structures and formats (JSON, XML, CSV etc) so a noSQL database is a good approach for a persistence layer. Analyzing sensors data evolutions could predict critical states in a system (infarction, stoke, hyperglycemic coma). The data analysis in noSQL databases is time-consuming and not effective in real time decision. This paper present a new layer as a persistence layer wrapper which will separate sensor data into two DBMS focused on different performances and purposes. All data will be stored in a noSQL database. Data used for critical states predictions will be selected and stored in a fast, SQL DBMS and adequate algorithms will be used to manage them.
- Mohammad Abdur Razzaque, Marija Milojevic-Jevric, Andrei Palade, Siobhan Clarke, Distrib. Middleware for Internet of Things: A Survey. IEEE Internet of Things Journal 3, 1 (2016), 70--95, ISSN: 2327-4662Google ScholarCross Ref
- Ting Lu, Jun Fang, Cong Liu. 2015. A Unified Storage and Query Optimization Framework for Sensor Data. 12th Web Information System and Application Conference (WISA), 11-13 Sept. 2015, 229--234, ISBN: 978-1-4673-9371-3 Google ScholarDigital Library
- Tingli Li. 2012. A Storage Solution for Massive IoT Data Based on NoSQL Green Computing and Communications (GreenCom) 2012 IEEE International Conference on Besancon, 50--57. Google ScholarDigital Library
- Xingjun Hao, X. Hao, Peiquan Jin, Lihua Yue. 2016.Efficient Storage of Multi-Sensor Object-Tracking Data. IEEE Transactions on Parallel and Distributed Systems, 27(10): 2881--2894, 2016. Google ScholarDigital Library
- C. L. Philip Chen and C. Y. Zhang. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences 275 (2014), 314--347.Google ScholarCross Ref
- E. Zdravevski, P. Lameski, V. Trajkovik, A. Kulakov, I. Chorbev, R. Goleva, N. Pombo, and N. Garcia. 2017. Improving activity recognition accuracy in ambient assisted living systems by automated feature engineering. IEEE Access 1, 1 ( 2017), 1--17.Google Scholar
- H. Leutheuser, D. Schuldhaus, and B. M. Eskofier. 2013. Hierarchical, multi-sensor based classification of daily life activities: Comparison with state-of-the-art algorithms using a benchmark dataset. PLoS ONE 8, 10 (2013), e75196.Google ScholarCross Ref
Index Terms
- Proposed Architecture to manage critical states predictions in IoT applications
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