The study deals with detection of the occupation of Intelligent Building (IB) using data obtained from indirect methods with Big Data Analysis within IoT. In the area of daily living activity monitoring, one of the most challenging tasks is occupancy prediction, giving us information about people's mobility in the building. This task can be done via monitoring of CO2 as a reliable method, which has the ambition to predict the presence of the people in specific areas. In this paper, we propose a novel hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity. For each such prediction, we also record the gold standard CO2 signal to objectively compare and evaluate the quality of the proposed system. Unfortunately, this prediction is often linked with a presence of predicted signal activities in the form of glitches, often having an oscillating character, which inaccurately approximates the real CO2 signals. Thus, the difference between the gold standard and the prediction results from SVM is increasing. Therefore, we employed as the second part of the proposed system a smoothing procedure based on Wavelet transformation, which has ambitions to reduce inaccuracies in predicted signal via smoothing and increase the accuracy of the whole prediction system. The whole system is completed with an optimization procedure based on the Artificial Bee Colony (ABC) algorithm, which finally classifies the wavelet's response to recommend the most suitable wavelet settings to be used for data smoothing.

Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 17 listopadu 15 Ostrava Poruba 708 00 Czech Republic

Vyjádření znepokojení v

Heliyon. 2025 Jan 30;11(3):e42380. doi: 10.1016/j.heliyon.2025.e42380. PubMed

Zobrazit více v PubMed

Rajan Sreeranga, Ginkel W.V., Sundaresan N., Bardhan Anant, Chen Y., Fuchs A., Kapre A., Lane A., Lu Rongxing, Manadhata Pratyusa, Molina J., Mora A.C., Murthy P., Roy Arnab, Sathyadevan Shiju, Shah Nrupak. Cloud Security Alliance Report on the Top Ten Challenges in Big Data Privacy and Security. 2013. https://doi.org/10.13140/RG.2.1.1744.1127 DOI

Spiegel S. In: Smart Information Systems. Hopfgartner F., editor. Springer International Publishing; Cham: 2015. Optimization of in-house energy demand; pp. 271–289. DOI

L. Brett, R. Love, L. Harvey, Big Data: Time for a lean approach in financial services, a Deloitte Analytics paper.

Fog Computing and the Internet of Things: Extend the Cloud to Where the Things Are.

Alotaibi B. Utilizing blockchain to overcome cyber security concerns in the Internet of things: a review. IEEE Sens. J. 2019;19(23):10953–10971. doi: 10.1109/JSEN.2019.2935035. DOI

Mohanty S.N., Ramya K., Rani S.S., Gupta D., Shankar K., Lakshmanaprabu S., Khanna A. An efficient lightweight integrated blockchain (ELIB) model for IoT security and privacy. Future Gener. Comput. Syst. 2020;102:1027–1037. doi: 10.1016/j.future.2019.09.050. DOI

Pešić S., Tošić M., Iković O., Radovanović M., Ivanović M., Bošković D. BLEMAT: data analytics and machine learning for smart building occupancy detection and prediction. Int. J. Artif. Intell. Tools. 2019;28(06) doi: 10.1142/S0218213019600054. DOI

Kim K., Jalal A., Mahmood M. Vision-based human activity recognition system using depth silhouettes: a smart home system for monitoring the residents. J. Electr. Eng. Technol. 2019;14(6):2567–2573. doi: 10.1007/s42835-019-00278-8. DOI

Longo E., Redondi A.E., Cesana M. Accurate occupancy estimation with WiFi and bluetooth/BLE packet capture. Comput. Netw. 2019;163 doi: 10.1016/j.comnet.2019.106876. DOI

Wang F., Feng Q., Chen Z., Zhao Q., Cheng Z., Zou J., Zhang Y., Mai J., Li Y., Reeve H. Predictive control of indoor environment using occupant number detected by video data and CO 2 concentration. Energy Build. 2017;145:155–162. doi: 10.1016/j.enbuild.2017.04.014. DOI

Ioannidis D., Tropios P., Krinidis S., Tzovaras D., Likothanassis S. In: Iliadis L., Maglogiannis I., editors. vol. 475. Springer International Publishing; Cham: 2016. Building multi-occupancy analysis and visualization through data intensive processing; pp. 587–599. (Artificial Intelligence Applications and Innovations). DOI

Akkaya K., Guvenc I., Aygun R., Pala N., Kadri A. 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) IEEE; New Orleans, LA, USA: 2015. IoT-based occupancy monitoring techniques for energy-efficient smart buildings; pp. 58–63. DOI

Demiris G. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; Minneapolis, MN: 2009. Privacy and social implications of distinct sensing approaches to implementing smart homes for older adults; pp. 4311–4314. PubMed DOI

Ni Q., García Hernando A., de la Cruz I. The Elderly's independent living in smart homes: a characterization of activities and sensing infrastructure survey to facilitate services development. Sensors. 2015;15(5):11312–11362. doi: 10.3390/s150511312. PubMed DOI PMC

Candanedo L.M., Feldheim V. Accurate occupancy detection of an office room from light, temperature, humidity and CO 2 measurements using statistical learning models. Energy Build. 2016;112:28–39. doi: 10.1016/j.enbuild.2015.11.071. DOI

Hobson B.W., Lowcay D., Gunay H.B., Ashouri A., Newsham G.R. Opportunistic occupancy-count estimation using sensor fusion: a case study. Build. Environ. 2019;159 doi: 10.1016/j.buildenv.2019.05.032. DOI

Arendt K., Johansen A., Jørgensen B.N., Kjærgaard M.B., Mattera C.G., Sangogboye F.C., Schwee J.H., Veje C.T. Proceedings of the First Workshop on Data Acquisition to Analysis. ACM; Shenzhen China: 2018. Room-level occupant counts, airflow and CO 2 data from an office building; pp. 13–14. DOI

Lachhab F., Bakhouya M., Ouladsine R., Essaaidi M. Context-driven monitoring and control of buildings ventilation systems using big data and Internet of things–based technologies, proceedings of the institution of mechanical engineers, Part I. J. Syst. Control Eng. 2019;233(3):276–288. doi: 10.1177/0959651818791406. DOI

Scott J., Bernheim Brush A., Krumm J., Meyers B., Hazas M., Hodges S., Villar N. Proceedings of the 13th International Conference on Ubiquitous Computing - UbiComp '11. ACM Press; Beijing, China: 2011. PreHeat: controlling home heating using occupancy prediction; p. 281. DOI

Wang W., Xu X., Wei H.-H., Ren B., Chen J. Modeling occupancy distribution in large building spaces for HVAC energy efficiency. Energy Proc. 2018;152:1230–1235. doi: 10.1016/j.egypro.2018.09.174. DOI

D'Oca S., Hong T. Occupancy schedules learning process through a data mining framework. Energy Build. 2015;88:395–408. doi: 10.1016/j.enbuild.2014.11.065. DOI

Crivello A., Mavilia F., Barsocchi P., Ferro E., Palumbo F. Detecting occupancy and social interaction via energy and environmental monitoring. Int. J. Sens. Netw. 2018;27(1):61. doi: 10.1504/IJSNET.2018.092136. DOI

Noury N., Berenguer M., Teyssier H., Bouzid M.-J., Giordani M. Building an index of activity of inhabitants from their activity on the residential electrical power line. IEEE Trans. Inf. Technol. Biomed. 2011;15(5):758–766. doi: 10.1109/TITB.2011.2138149. PubMed DOI

Pyle D. Morgan Kaufmann Publishers; San Francisco, Calif: 1999. Data Preparation for Data Mining.

García S., Luengo J., Herrera F. vol. 72. Springer International Publishing; Cham: 2015. Data Preprocessing in Data Mining. (Intelligent Systems Reference Library). DOI

García S., Luengo J., Herrera F. Springer International Publishing; Cham: 2015. Data Preparation Basic Models, vol. 72; pp. 39–57. DOI

Eitrich T., Lang B. Efficient optimization of support vector machine learning parameters for unbalanced datasets. Int. J. Comput. Appl. Math. 2006;196(2):425–436. doi: 10.1016/j.cam.2005.09.009. DOI

Farahani B., Firouzi F., Chakrabarty K. In: Intelligent Internet of Things. Firouzi F., Chakrabarty K., Nassif S., editors. Springer International Publishing; Cham: 2020. Healthcare IoT; pp. 515–545. DOI

Ho Y., Pepyne D. Simple explanation of the no-free-lunch theorem and its implications. J. Optim. Theory Appl. 2002;115(3):549–570. doi: 10.1023/A:1021251113462. DOI

Wolpert D., Macready W. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1997;1(1):67–82. doi: 10.1109/4235.585893. DOI

Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg Scikit-learn V. Machine learning in Python. J. Mach. Learn. Res. 2011;12:2825–2830.

Vanus J., Gorjani O.M., Bilik P. Novel proposal for prediction of CO2 course and occupancy recognition in intelligent buildings within IoT. Energies. 2019;12(23):4541. doi: 10.3390/en12234541. DOI

Vanus J., Martinek R., Danys L., Nedoma J., Bilik P. Occupancy detection in smart home space using interoperable building automation technologies. Hum.-Cent. Comput. Inf. Sci. 2022;12(1):616–632. doi: 10.22967/HCIS.2022.12.047. DOI

Vanus J., Majidzadeh Gorjani O., Dvoracek P., Bilik P., Koziorek J. Application of a new CO2 prediction method within family house occupancy monitoring. IEEE Access. 2021;9:158760–158772. doi: 10.1109/ACCESS.2021.3130216. DOI

Vanus J., Fiedorova K., Kubicek J., Gorjani O.M., Augustynek M. Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of things. Sensors. 2020;20(3):620. doi: 10.3390/s20030620. PubMed DOI PMC

Vanus J., Kubicek J., Gorjani O.M., Koziorek J. Using the IBM SPSS SW tool with wavelet transformation for CO2 prediction within IoT in smart home care. Sensors. 2019;19(6):1407. doi: 10.3390/s19061407. PubMed DOI PMC

Mena A.R., Ceballos H.G., Alvarado-Uribe J. Measuring indoor occupancy through environmental sensors: a systematic review on sensor deployment. Sensors. 2022;22(10):3770. doi: 10.3390/s22103770. PubMed DOI PMC

Ali S., Bouguila N. Towards scalable deployment of hidden Markov models in occupancy estimation: a novel methodology applied to the study case of occupancy detection. Energy Build. 2022;254 doi: 10.1016/j.enbuild.2021.111594. DOI

Guo J., Amayri M., Bouguila N., Fan W. A hybrid of interactive learning and predictive modeling for occupancy estimation in smart buildings. IEEE Trans. Consum. Electron. 2021;67(4):285–293. doi: 10.1109/TCE.2021.3131943. DOI

Huang Q., Rodriguez K., Whetstone N., Habel S. Rapid Internet of things (IoT) prototype for accurate people counting towards energy efficient buildings. Electron. J. Inf. Tech. Constr. 2019;24:1–13. doi: 10.36680/j.itcon.2019.001. DOI

Shirsat K.P., Bhole G.P. In: Proceedings of International Conference on Sustainable Expert Systems, vol. 176. Shakya S., Balas V.E., Haoxiang W., Baig Z., editors. Springer Singapore; Singapore: 2021. An empirical study on the occupancy detection techniques based on context-aware IoT system; pp. 95–105. DOI

Howedi A., Lotfi A., Pourabdollah A. Employing entropy measures to identify visitors in multi-occupancy environments. J. Ambient Intell. Humaniz. Comput. 2022;13(2):1093–1106. doi: 10.1007/s12652-020-02824-z. DOI

Huang Q. Occupancy-driven energy-efficient buildings using audio processing with background sound cancellation. Buildings. 2018;8(6):78. doi: 10.3390/buildings8060078. DOI

Huang T., Zhao R., Bi L., Zhang D., Lu C. Neural embedding singular value decomposition for collaborative filtering. IEEE Trans. Neural Netw. Learn. Syst. 2021:1–9. doi: 10.1109/TNNLS.2021.3070853. PubMed DOI

Das A., Gupta R., Chakraborty S. 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS) IEEE; Bengaluru, India: 2020. A study on real-time edge computed occupancy estimation in an indoor environment; pp. 527–530. DOI

Sun Z., Yang J., Li X. Differentially private singular value decomposition for training support vector machines. Comput. Intell. Neurosci. 2022;2022:1–11. doi: 10.1155/2022/2935975. PubMed DOI PMC

Skön J.P., Johansson M., Raatikainen M., Leiviskä K., Kolehmainen M. Modelling indoor air carbon dioxide (CO2) concentration using neural network. Methods. 2012;14(15):16.

Huang Qiujun, Mao Jingli, Liu Yong. 2012 IEEE 14th International Conference on Communication Technology. IEEE; Chengdu, China: 2012. An improved grid search algorithm of SVR parameters optimization; pp. 1022–1026. DOI

Li S., Fang H., Liu X. Parameter optimization of support vector regression based on sine cosine algorithm. Expert Syst. Appl. 2018;91:63–77. doi: 10.1016/j.eswa.2017.08.038. DOI

Zhang D., Liu W., Wang A., Jin H. 2010 Chinese Conference on Pattern Recognition (CCPR) IEEE; Chongqing, China: 2010. Parameter optimization for SVR based on genetic algorithm and simplex method; pp. 1–6. DOI

Pan Kongzhi-Lilun-Zhuanye-Weiyuanhui Q., editor. 2013 32nd Chinese Control Conference; (CCC 2013): Xi'an, China, 26 - 28 July 2013; Piscataway, NJ: IEEE; 2013.

Pasolli L., Notarnicola C., Bruzzone L. Multi-objective parameter optimization in support vector regression: general formulation and application to the retrieval of soil moisture from remote sensing data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012;5(5):1495–1508. doi: 10.1109/JSTARS.2012.2197178. DOI