Abstract
Contemporary world rest on wireless connections via networks developed rapidly. Majority of people using wireless networks i.e. WiFi at: work, home, and also when they are outside. A central issue in wireless networks is efficiency and stable connection to Internet. Additional challenge is to create and maintain reliable WiFi network with full open access, videlicet every user could connect with that network if only have a range. Exemplary aforesaid network is university network where users are very specific group. An innovative approach is to treat and use Access Points (APs) as wireless sensors network to crowd monitoring in buildings where such network is located. Thanks to this we receive accurate information about the number of users (people) without interfering with their sensitive data. The aim of this research is to use geostatistical methods to prepare spatial models and predict behaviour of crowd connected with wireless sensors on full of range considered area. The investigation has tended to focus on efficiency of wireless sensors belong to open WiFi network named PWR-WiFi located in building at the main campus of Wrocław University of Science and Technology (WUST) in Poland. The data gathered for analysis are acquired from three consecutive years 2014–2016 to better show the dynamic growth of number of PWR-WiFi network users it means number of people crowded in building. Parameter, which most reflecting behaviour of PWR-WiFi network is the number of users, obtained from APs, that was investigated during research. Preliminary and structural analysis with approximation of variogram models were made and as the next step spatial prediction models of wireless sensors network efficiency were performed by Turning Bands geostatistical simulation method. Three models of spatial prediction were prepared for three subsequent years 2014, 2015 and 2016. Following, the results were compared with spatial prediction models created previously by ordinary kriging estimation method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Katsaros, D., Manolopoulos, Y.: Prediction in wireless networks by Markov chains. IEEE Wirel. Commun. 16(2), 2–9 (2009)
Pan, D.: Analysis of Wi-Fi performance data for a Wi-Fi throughput prediction approach. MSc Thesis. KTH Royal Institute of Technology School of Information and Communication Technology, Stockholm (2017)
Kulkarni, P., Lewis, T., Fan, Z.: Simple traffic prediction mechanism and its applications in wireless networks. Wirel. Pers. Commun. 59, 261–274 (2011)
Odabasi, S.D., Gumus, E.: A prediction model for performance analysis in wireless mesh networks. Int. J. Electron. Mech. Mechatr. Eng. 6(3), 1241–1250 (2016)
Prasad, P.S., Agrawal, P.: Movement prediction in wireless networks using mobility traces. In: 2010 7th IEEE Consumer Communications and Networking Conference (CCNC) (2010). https://doi.org/10.1109/CCNC.2010.5421613
Ananthi, J., Ranganathan, V.: Review: on mobility prediction for wireless networks. Int. J. Emerg. Technol. Adv. Eng. 3(4), 891–902 (2013)
Wu, T., Tsai, C., Guo, J.: LiDAR/camera sensor fusion technology for pedestrian detection. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, pp. 1675–1678 (2017)
Kamińska-Chuchmała, A., Graña, M.: Indoor crowd 3D localization in big buildings from Wi-Fi access anonymous data. Sensors 19(19), 4211 (2019). https://doi.org/10.3390/s19194211
Borzemski, L., Kamińska-Chuchmała, A.: Client-perceived web performance knowledge discovery through turning bands method. Cybern. Syst. Int. J. 43(4), 354–368 (2012)
Borzemski, L., Kamińska-Chuchmała, A.: Knowledge engineering relating to spatial web performance forecasting with sequential Gaussian simulation method. In: Advances in Knowledge-Based and Intelligent Information and Engineering Systems. FAIA, vol. 243, pp. 1439–1448. IOS Press, Amsterdam (2012)
Borzemski, L., Kamińska-Chuchmała, A.: Distributed web systems performance forecasting using turning bands method. IEEE Trans. Industr. Inf. 9(1), 254–261 (2013)
Kamińska-Chuchmała, A.: Spatial Internet traffic load forecasting with using estimation method. Procedia Comput. Sci. 35, 290–298 (2014)
Chentsov, N.N.: Lévy Brownian motion for serveral parameters and generalized white noise. Theor. Prob. Appl. 2(2), 265–266 (1957)
Matern, B.: Spatial variation – stochastic models and their application to some problems in forests surveys and other sampling investigations. In: Meddelanden fran Statens Skogsforskningsinstitut, vol. 48(5). Almaenna Foerlaget, Stockholm (1960)
Matheron, G.: The intrinsic random functions and their applications. Adv. Appl. Prob. 5, 439–468 (1973)
Chiles, J.-P., Delfiner, P.: Geostatistics: Modeling Spatial Uncertainty. Wiley, Hoboken (2012)
Lantuejoul, C.: Geostatistical Simulation. Models and Algorithms. Springer, Heidelberg (2002)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2018). https://www.R-project.org
Renard, D., Bez, N., Desassis, N., Beucher, H., Ors, F., Freulon, X.: RGeostats: The Geostatistical R package 11.2.2 MINES ParisTech/ARMINES (2018). http://cg.ensmp.fr/rgeostats
Kamińska-Chuchmała, A.: Spatial prediction models of wireless network efficiency estimated by kriging method. Rynek Energii 135(2), 89–94 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kamińska-Chuchmała, A. (2021). Efficiency of Public Wireless Sensors Applied to Spatial Crowd Monitoring in Buildings. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_81
Download citation
DOI: https://doi.org/10.1007/978-3-030-57802-2_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57801-5
Online ISBN: 978-3-030-57802-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)