Abstract
The rapidly emerging Internet of Things supports many diverse applications including environmental monitoring. Air quality, both indoors and outdoors, proved to be a significant comfort and health factor for people. This paper proposes a smart context-aware system for indoor air quality monitoring and prediction called DisCPAQ. The system uses data streams from air quality measurement sensors to provide real-time personalised air quality service to users through a mobile app. The proposed system is agnostic to sensor infrastructure. The paper proposes a context model based on Context Spaces Theory, presents the architecture of the system and identifies challenges in developing large scale IoT applications. DisCPAQ implementation, evaluation and lessons learned are all discussed in the paper.
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Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I.: Internet of Things: vision, applications and research challenges. Ad Hoc Netw. 10(7), 1497–1516 (2012)
Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999). doi:10.1007/3-540-48157-5_29
Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the Internet of Things: a survey. IEEE Commun. Surv. Tutorials 16(1), 414–454 (2014)
Air Quality Index: A guide to air quality and your health. Washington, USEPA Air and Radiation, Environmental Protection Agency, EPA-454/K-03-002, vol. 19, pp. 11–01 (2003)
Brown, S.: Indoor Air Quality. Environment Australia (1997)
Kim, J.-Y., Chu, C.-H., Shin, S.-M.: ISSAQ: an integrated sensing systems for real-time indoor air quality monitoring. IEEE Sens. J. 14(12), 4230–4244 (2014)
Kang, B., Park, S., Lee, T., Park, S.: IoT-based monitoring system using tri-level context making model for smart home services. In: 2015 IEEE International Conference on Consumer Electronics (ICCE), pp. 198–199. IEEE (2015)
Fang, B., Xu, Q., Park, T., Zhang, M.: AirSense: an intelligent home-based sensing system for indoor air quality analytics. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 109–119. ACM (2016)
Kyriazakos, S., Mihaylov, M., Anggorojati, B., Mihovska, A., Craciunescu, R., Fratu, O., Prasad, R.: eWALL: an intelligent caring home environment offering personalized context-aware applications based on advanced sensing. Wirel. Pers. Commun. 87(3), 1093–1111 (2016)
Canada. Service de l’environnement atmosphérique, Masterton, J., Richardson, F.: Humidex: a method of quantifying human discomfort due to excessive heat and humidity. Downsview, Ontario: Atmospheric Environment (1979)
Padovitz, A., Loke, S.W., Zaslavsky, A.: Towards a theory of context spaces. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 38–42. IEEE (2004)
Gouveia, N., Fletcher, T.: Time series analysis of air pollution and mortality: effects by cause, age and socioeconomic status. J. Epidemiol. Commun. Health 54(10), 750–755 (2000)
Boytsov, A., Zaslavsky, A.: ECSTRA – distributed context reasoning framework for pervasive computing systems. In: Balandin, S., Koucheryavy, Y., Hu, H. (eds.) NEW2AN/ruSMART-2011. LNCS, vol. 6869, pp. 1–13. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22875-9_1
Klimova, A., Rondeau, E., Andersson, K., Porras, J., Rybin, A.V., Zaslavsky, A.: An international master’s program in green ICT as a contribution to sustainable development. J. Cleaner Prod. 135, 223–239 (2016)
Acknowledgment
The research reported here was supported and funded by the PERCCOM Erasmus Mundus Program of the European Union [14]. Part of this work has been carried out in the scope of the project bIoTope, which is co-funded by the European Commission under Horizon-2020 program, contract number H2020-ICT-2015/ 688203-bIoTope. The research has been carried out with the financial support of the Ministry of Education and Science of the Russian Federation under grant agreement RFMEFI58716X0031.
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Belyakhina, T., Zaslavsky, A., Mitra, K., Saguna, S., Jayaraman, P.P. (2017). DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. ruSMART NsCC NEW2AN 2017 2017 2017. Lecture Notes in Computer Science(), vol 10531. Springer, Cham. https://doi.org/10.1007/978-3-319-67380-6_7
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DOI: https://doi.org/10.1007/978-3-319-67380-6_7
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