Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F381

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 1157–1166 (2017)

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Abstract. IoT applications has some characteristics that set it apart from other fields mainly due to the multitude of different types of sensors producing data. In monitoring applications, data processing requires real-time or soft real-time responses in order to aid systems to make important decisions but also predictive analysis to leverage the potential of IoT by data mining vast datasets. This paper presents an architecture developed to efficiently process and store data coming from an huge number of distributed IoT sensors. The back-end of SeeYourBox services is currently based on the proposed architecture that has proven to be stable and meet all the requirements.

References

1. A115. How cloud-powered FinTech start-ups are disrupting the banks. 2016. http://a115.co.uk/publications/awsfintech-startups.html.
2. Inc. Aerospike. What is a Key-Value Store? 2016. http://www.aerospike.com/what-is-a-key-value-store/
3. Luigi Atzori, Antonio Iera, and Giacomo Morabito. "The Internet of Things: A survey". In: Computer Networks 54.15 (2010), pp. 2787 -2805. ISSN: 1389-1286. http://dx.doi.org/http://dx.doi.org/10.1016/j.comnet.2010.05.010. http://www.sciencedirect.com/science/article/pii/S1389128610001568
4. Amazon AWS. About Us. 2016. https://aws.amazon.com/about-aws/
5. Amazon AWS. What Is AWS IoT? 2016. http://docs.aws.amazon.com/iot/latest/developerguide/whatis-aws-iot.html
6. Galip Aydin, Ibrahim Riza Hallac, and Betul Karakus. "Architecture and Implementation of a Scalable Sensor Data Storage and Analysis System Using Cloud Computing and Big Data Technologies". In: Journal of Sensors 2015 (2015), p. 11. URL: 10.1155/2015/834217.
7. V. Carchiolo at Al. "Users’ attachment in trust networks: reputation vs. effort". In International Journal of Bio-Inspired Computation, 2013, pp. 199–209, ISSN: 1758-0366. http://dx.doi.org/10.1504/IJBIC.2013.055450
8. A. Chianese, F. Piccialli, and G. Riccio. "SMuNe: A Smart Multi-sensor Network Based on Embedded Systems in IoT Environment". In: 2015 11th International Conference on Signal-Image Technology Internet-Based Systems (SITIS). 2015, pp. 841-848. http://dx.doi.org/10.1109/SITIS.2015.51.
9. CompareBusinessProducts.com. Top 10 Largest Databases in the World. http://www.comparebusinessproducts.com/fyi/10-largest-databases-in-the-world
10. Microsoft Corporation. Microsoft Azure IoT Reference Architecture. 2016.
11. DB-Engines. DB-Engines Ranking. 2016. http://db-engines.com/en/ranking
12. Bryan Helmig. Why Task Queues - ComoRichWeb. 2012. http://www.slideshare.net/bryanhelmig/task-queuescomorichweb-12962619.
13. Marc Jadoul. How Big is the Internet of Things? 2016. http://www.business2community.com/business-innovation/big-internet-things-01593563
14. L. Jiang et al. "An IoT-Oriented Data Storage Framework in Cloud Computing Platform". In: IEEE Transactions on Industrial Informatics 10.2 (2014), pp. 1443-1451. ISSN: 1551-3203. http://dx.doi.org/10.1109/TII.2014.2306384.
15. J. Jin Kang et al. "Predictive data mining for Converged Internet of Things: A Mobile Health perspective". In: Telecommunication Networks and Applications Conference (ITNAC), 2015 International. 2015, pp. 5-10. http://dx.doi.org/10.1109/ATNAC.2015. 7366781.
16. T. Li et al. "A Storage Solution for Massive IoT Data Based on NoSQL". In: Green Computing and Communications (GreenCom), 2012 IEEE International Conference on. 2012, pp. 50-57. http://dx.doi.org/10.1109/GreenCom.2012.18.
17. DigitalOceanTM Inc. O.S. Tezer. SQLite vs MySQL vs PostgreSQL: A Comparison Of Relational Database Management Systems. 2014.
18. C. Perera et al. "Context Aware Computing for The Internet of Things: A Survey". In: IEEE Communications Surveys Tutorials 16.1 (2014), pp. 414-454. ISSN: 1553-877X. http://dx.doi.org/10.1109/ SURV.2013.042313.00197.
19. T. A. M. Phan, J. K. Nurminen, and M. Di Francesco. "Cloud Databases for Internet-of-Things Data". In: Internet of Things (iThings), 2014 IEEE International Conference on, and Green Computing and Communications (GreenCom), IEEE and Cyber, Physical BIBLIOGRAPHY 53 and Social Computing(CPSCom), IEEE. 2014, pp. 117-124. http://dx.doi.org/10.1109/iThings.2014.26.
20. Evangelos Psomakelis et al. "Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications". In: CoRR abs/1607.00509 (2016). http://arxiv.org/abs/1607.00509.
21. Redis. Redis Documentation. 2016. http://redis.io/
22. C. Rommel at al.. Amazon AWS & Microsoft Azure IoT Deep Dive. 2016.
23. Bryce Merkl Sasaki. Graph Databases for Beginners: ACID vs. BASE Explained. 2015. https://neo4j.com/blog/acidvs-base-consistency-models-explained/
24. W. Shi and M. Liu. "Tactics of handling data in Internet of things". In: 2011 IEEE International Conference on Cloud Computing and Intelligence Systems. 2011, pp. 515-517. http://dx.doi.org/10.1109/ CCIS.2011.6045121.
25. F. Xhafa et al. "A Software Chain Approach to Big Data Stream Processing and Analytics". In: Complex, Intelligent, and Software Intensive Systems (CISIS), 2015 Ninth International Conference on. 2015, pp. 179-186. http://dx.doi.org/10.1109/CISIS.2015.24