Sensor Information Quality for Situation Management: a case study in thermal monitoring

• Allan F. F. Amaral IFES
• Matheus O. Jagi IFES
• José Gonçalves Pereira Filho UFES
• Silvana Rossetto UFRJ
• João L. R. Moreira University of Twente

DOI: https://doi.org/10.5753/sbcup.2017.3304

Abstract

Ubiquitous and pervasive computing enables context-sensitive monitoring applications to use sensor networks as data sources, thereby linking the physical world to the information world. Before being consumed, the data collected needs to be evaluated for their quality dimensions. With these metadata, applications can decide to discard or use the data to detect situations of interest, which may vary over time and according to the domain of each application. In this article, we present an architecture for WSN-based monitoring applications that introduces integrated support for quality of information (QoI) and management of situations. The validation of this architecture is described with a case study in thermal monitoring domain.

References

AL-FUQAHA, A. et al. Internet of things: A survey on enabling technologies, protocols, and applications. Communications Surveys & Tutorials, IEEE, 17(4):2347–2376, 2015.

ABID, A.; KACHOURI, A.; MAHFOUDHI, A. Anomaly Detection in WSN: critical study with new vision. International Conference on Automation, Control, Engineering and Computer Science (ACECS’14) Proceedings, p. 1–9, 2014.

BALDAUF, M.; DUSTDAR, S.; ROSENBERG, F. A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, v. 2, n. 4, p. 263, 2007.

BISDIKIAN, C.; KAPLAN, L. M.; SRIVASTAVA, M. B. On the Quality and Value of Information in Sensor Networks. ACM Transactions on Sensor Networks (TOSN), v. 9, n. 4, p. 1–26, 2013.

COSTA, P. D. et al. Rule-Based Support for Situation Management. In: ROGOVA, G.; SCOTT, P. (Eds.). Fusion Methodologies in Crisis Management. Cham: Springer International Publishing, 2016. p. 341–364.

DEY, A. K. Understanding and using context. Personal and Ubiquitous Computing, v. 5, n. 1, p. 4–7, 2001.

FAWZY, A.; MOKHTAR, H. M. O.; HEGAZY, O. Outliers Detection and Classification in Wireless Sensor Networks. Egyptian Informatics Journal, v. 14, n. 2, p. 157–164, 2013.

GUO, H. et al. A Flexible Framework for Assessing the Quality of Information in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, v. 2015, p. 1–15, 2015.

MIORANDI, D. et al. Internet of things: Vision, applications and research challenges. Ad Hoc Networks, v. 10, n. 7, p. 1497–1516, 2012.

MOREIRA, J. L. R. et al. Ontology-driven Conceptual Modeling for Early Warning Systems: Redesigning the Situation Modeling Language. n. Modelsward, p. 467–477, 2015.

NISHA, U. B. et al. Statistical Based Outlier Detection in Data Aggregation for Wireless Sensor Networks. Journal of Theoretical and Applied Information Technology, v. 59, n. 3, p. 770–780, 2014.

OLIVEIRA, E. C. DE. Comparação das diferentes técnicas para a exclusão de “outliers”. Metrologia, 2008.

PEREIRA, I. S. A.; COSTA, P. D.; ALMEIDA, J. P. A. A rule-based platform for situation management. IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA). Anais. IEEE, fev. 2013.

SACHIDANANDA, V. et al. Quality of Information in Wireless Sensor Networks: A Survey. Proceedings of the 15th International Conference on Information Quality (ICIQ-2010), v. 1, p. 1–15, 2010.

SU, L. Resource Efficient Information Integration In Cyber-Physical Systems. [s.l.] Universidade de Illinois, Urbana-Champaingn, 2013.

YANG ZHANG; MERATNIA, N.; HAVINGA, P. Outlier Detection Techniques for Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, v. 12, n. 2, p. 159–170, 2010.

ZHOU, P. et al. Wireless sensor network based monitoring system for a large-scale indoor space: Data process and supply air allocation optimization. Energy and Buildings, v. 103, p. 365–374, 2015.