Open Access
| Issue |
MATEC Web Conf.
Volume 125, 2017
21st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|
|
|---|---|---|
| Article Number | 05010 | |
| Number of page(s) | 5 | |
| Section | Signal Processing | |
| DOI | https://doi.org/10.1051/matecconf/201712505010 | |
| Published online | 04 October 2017 | |
- J. Lloret, A. Canovas, S. Sendra, L. Parra, A smart communication architecture for ambient assisted living, IEEE Commun. Mag., 53(1), p. 26–33 (2015) [CrossRef] [Google Scholar]
- K. Akkaya, I. Guvenc, R. Aygun, N. Pala, A. Kadri, IoT-based occupancy monitoring techniques for energy-efficient smart buildings, Wireless Communications and Networking Conference Workshops (WCNCW), 2015, p. 58–63 (2015) [Google Scholar]
- A. Kumar, G. P. Hancke, An energy-efficient smart comfort sensing system based on the IEEE 1451 standard for green buildings, IEEE Sens. J., 14(12), p. 4245–4252 (2014) [CrossRef] [Google Scholar]
- S. C. Folea, G. Mois, A low-power wireless sensor for online ambient monitoring, IEEE Sens. J., 15(2), p. 742–749 (2015) [CrossRef] [Google Scholar]
- S. Abraham, X. Li, A cost-effective wireless sensor network system for indoor air quality monitoring applications, Procedia Comput. Sci., 34, p. 165–171 (2014) [CrossRef] [Google Scholar]
- M. Kotol, A. Heller, C. Orthmann, Introduction of flexible monitoring equipment into the Greenlandic building sector, ARTEK Event 2014 (2014) [Google Scholar]
- S. K. Wang, S. P. Chew, M. T. Jusoh, A. Khairunissa, K. Y. Leong, A. A. Azid, WSN based indoor air quality monitoring in classrooms, AIP Conference Proceedings, 1808(1), p. 20063 (2017) [CrossRef] [Google Scholar]
- G. de Gennaro, P. R. Dambruoso, A. D. Loiotile, A. Di Gilio, P. Giungato, M. Tutino, A. Marzocca, A. Mazzone, J. Palmisani, F. Porcelli, Indoor air quality in schools, Environ. Chem. Lett., 12(4), p. 467–482 (2014) [CrossRef] [Google Scholar]
- T. Vehviläinen, H. Lindholm, H. Rintamäki, R. Pääkkönen, A. Hirvonen, O. Niemi, J. Vinha, High indoor CO2 concentrations in an office environment increases the transcutaneous CO2 level and sleepiness during cognitive work, J. Occup. Environ. Hyg. 13(1), p. 19–29 (2016) [CrossRef] [Google Scholar]
- K. Tijani, S. Ploix, B. Haas, J. Dugdale, Q. D. Ngo, Dynamic Bayesian Networks to simulate occupant behaviours in office buildings related to indoor air quality, arXiv Prepr. arXiv1605.05966 (2016) [Google Scholar]
- Q. Huang, C. Mao, Occupancy estimation in smart building using hybrid CO2/light wireless sensor network, J. Appl. Sci. Arts, 1(2), p. 5 (2017) [EDP Sciences] [Google Scholar]
- G. Diraco, A. Leone, P. Siciliano, People occupancy detection and profiling with 3D depth sensors for building energy management, Energy and Buildings, 92, p. 246–266 (2015) [CrossRef] [Google Scholar]
- X. Guo, D. K. Tiller, G. P. Henze, C.E. Waters, The performance of occupancy-based lighting control systems: A review, Lighting Research & Technology, 42(4), p. 415–431 (2010) [CrossRef] [Google Scholar]
- M. Jin, N. Bekiaris-Liberis, K. Weekly, C. Spanos, A. Bayen, Sensing by proxy: Occupancy detection based on indoor CO2 concentration, UBICOMM 2015, p. 14 (2015) [Google Scholar]
- Z. Chen, Q. Zhu, M. Masood, C. S. Yeng, Environmental Sensors based Occupancy Estimation in Buildings via IHMM-MLR, IEEE Trans. Ind. Informatics (2017) [Google Scholar]
- D. Wörner, T. von Bomhard, M. Röschlin, F. Wortmann, Look twice: Uncover hidden information in room climate sensor data, 2014 International Conference on the Internet of Things, p. 25–30 (2014) [Google Scholar]
- ISO/IEC Standard 14543-3-10, International standard ISO/IEC 14543-3-10: Wireless Short-Packet (WSP) protocol optimized for energy harvesting—architecture and lower layer protocols, edition 1.0 (2012) [Google Scholar]
- R Core Team, R: A Language and Environment for Statistical Computing, Vienna, Austria (2016) [Google Scholar]
- M. Kuhn C. from Jed Wing, S. Weston, A. Williams, C. Keefer, A. Engelhardt, T. Cooper, Z. Mayer, B. Kenkel, the R Core Team, M. Benesty, R. Lescarbeau, A. Ziem, L. Scrucca, Y. Tang, C. Candan, caret: Classification and Regression Training (2016) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.