Abstract
An institution’s academic buildings are an important meeting point for students, and the crowd flow affects its ecological parameters. Therefore, it is necessary to measure the ecological parameters and crowd flow in such a building. Tracking the density of large-scale people and environmental parameters is a challenging task. In this study, a lightweight smartphone-based crowd flow measurement (SP-SP-CFM) is introduced for tracking the crowd flow in academic buildings. The proposed SP-CFM searches the requests enabled by the user’s smartphone at regular intervals. SP-CFM has been implemented, and a different test has been carried out in the academic building in real-world settings. The SP-CFM was deployed in different locations, including the evacuation passage on the first floor and two classrooms. The ecological parameters, such as temperature and CO2 concentration, have been studied at different locations in the evacuation passage. The effect of people’s walking behaviour has been studied in four different modes of smartphone operation. The performance of the suggested SP-CFM is evaluated with different models of smartphones with variable walking speeds. The results show the proposed SP-CFM provides a tracking accuracy of 93.6% in the Wi-Fi registered mode.
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21 November 2023
A Correction to this paper has been published: https://doi.org/10.1007/s42486-023-00142-9
References
“Meshlium xtreme,” [Online]. Available: http://www.libelium.com/products/meshlium (2018)
Anathi, M., Vijayakumar, K.: An intelligent approach for dynamic network traffic restriction using MAC address verification. Comput. Commun. 154, 559–564 (2020). https://doi.org/10.1016/j.comcom.2020.02.021
Asahiro, Y., Jansson, J., Lin, G., Miyano, E., Ono, H., Utashima, T.: Exact algorithms for the repetition-bounded longest common subsequence problem. Theoret. Comput. Sci. 838, 238–249 (2020). https://doi.org/10.1016/j.tcs.2020.07.042
Barbera, M.V., Epasto, A., Mei, A., Perta, V.C., Stefa, J:. Signals from the crowd: Uncovering social relationships through smartphone probes. In Proc. Conf. Internet Meas. Conf. 2013;265–276. DOI: https://doi.org/10.1145/2504730.2504742
Beal, M.J., Jojic, N., Attias, H.: A graphical model for audiovisual object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25, 7 (2003). https://doi.org/10.1109/TPAMI.2003.1206512
Booth, A., Bosher, L., Chmutina, K.: The protection of crowded places from terrorist threats: does protective security advice meet the needs of security managers? Secur J. 2, 1–14 (2022). https://doi.org/10.1057/s41284-022-00332-7
Chellaswamy, C., Muthammal, R., Geetha, T.S.: A new methodology for optimal rail track condition measurement using acceleration signals. Meas. Sci. Technol. 29(7), 1–16 (2018). https://doi.org/10.1088/1361-6501/aabe48
Chellaswamy, C., Srinivasan, S., Babu, R.R.: A humanitarian intelligent level crossing controller utilizing GPS and GSM, IEEE International Conference on Global Humanitarian Technology Conference—South Asia Satellite (GHTC-SAS), 245 – 250 (2014). https://doi.org/10.1109/GHTC-SAS.2014.6967591
Chen, L., Wei, J., Ferryman, A.: A survey of human motion analysis using depth imagery. Pattern Recogn. Lett. 34(15), 1995–2006 (2013). https://doi.org/10.1016/j.patrec.2013.02.006
Deak, G., Curran, K., Condell, J.: A survey of active and passive indoor localisation systems. Comput. Commun. 35(16), 1939–1954 (2012). https://doi.org/10.1016/j.comcom.2012.06.004
Delcea, C., Cotfas, L.A.: Increasing awareness in classroom evacuation situations using agent-based modelling. Phys. a. 523, 1400–1418 (2019). https://doi.org/10.1016/j.physa.2019.04.137
Manisha, A., Dudhedia, Y.R.: Performance analysis of game based MAC protocol for cognitive radio based wireless network. J. King Saud Univ. Comput. Inform. Sci. 2021;2:1–17. Doi: https://doi.org/10.1016/j.jksuci.2020.12.018
Guvensan, M.A., Yavuz, A.G.: On coverage issues in directional sensor networks: A survey. Ad Hoc Netw. 9(7), 1238–1255 (2011). https://doi.org/10.1016/j.adhoc.2011.02.003
Hirschberg, D.S.: Algorithms for the longest common subsequence problem. J. ACM 24(4), 664–675 (1977)
Hu, Y., Wang, X., Liu, A.: CPSS approach for emergency evacuation in building fires. IEEE Intell. Syst. 29(3), 48–52 (2014). https://doi.org/10.1109/MIS.2014.38
Hudec, P., Polivka, M., Pechac, P.: Microwave system for the detection and localization of mobile phones in large buildings. IEEE Trans. Microw. Theory Tech. 53(6), 2235–2239 (2005). https://doi.org/10.1109/TMTT.2005.848750
Jiang, H., Liao, S., Li, J., Prinet, V., Xiang, S.: Urban scene based semantical modulation for pedestrian detection. Neurocomputing 474, 1–12 (2022). https://doi.org/10.1016/j.neucom.2021.11.091
Khoche, S., Chandrasekhar, K.V., Sasirekha, G.V.K., et al.: Occupancy detection for emergency management of smart building based on indoor localization. SN Comput. Sci. 2, 1–16 (2021). https://doi.org/10.1007/s42979-021-00812-4
Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010). https://doi.org/10.1109/MCOM.2010.5560598
Le, Su., Liao, L., Zhai, W., Xia, S.: Data-driven human model estimation for realtime motion capture. J. vis. Lang. Comput. 48, 10–18 (2018). https://doi.org/10.1016/j.jvlc.2018.05.001
Li, K., et al.: Understanding crowd density with a smartphone sensing system. In Proc. IEEE World Forum Internet Things. 2, 517–522 (2018). https://doi.org/10.1109/WF-IoT.2018.8355126
Li, K., Yuen, C., Kanhere, S.: SenseFlow: An experimental study of people tracking. In Proc. 6th ACM Workshop Real World Wireless Sensor Netw. 31–34. (2015) https://doi.org/10.1109/JSYST.2018.2880028
Lian, L.P., Song, W.G., Kwok, K.R., Luciano, T.: Analysis of repulsion states among pedestrians inflowing into a room. Phys. Lett. a. 382, 2424–2430 (2018). https://doi.org/10.1016/j.physleta.2018.05.055
Lopez-Carmona, M.A.: System identification for the design of behavioral controllers in crowd evacuations. Transp. Res. Part C: Emerg. Technol. 144, 103913 (2022). https://doi.org/10.1016/j.trc.2022.103913
Luo, Y., Yu, Z., Yin, H., et al.: Multi-agent mobile crowd sensing by pervasive machines: A robust task allocation approach. CCF Trans. Pervasive Comp. Interact. (2022). https://doi.org/10.1007/s42486-022-00104-7
Mozos, O.M., Kurazume, R., Hasegawa, T.: Multi-part people detection using 2D range data. Int. J. Social Robot 2(1), 31–40 (2010). https://doi.org/10.1007/s12369-009-0041-3
Najmanová, H., Ronchi, E.: An experimental data-set on pre-school children evacuation. Fire Technol. 53, 1509–1533 (2017). https://doi.org/10.1007/s10694-016-0643-x
Roy, P., Chowdhury, C.: A survey on ubiquitous WiFi-based indoor localization system for smartphone users from implementation perspectives. CCF Trans. Pervasive Comp. Interact. 4, 298–318 (2022). https://doi.org/10.1007/s42486-022-00089-3
Ruiz-Ruiz A.J., Blunck, H., Prentow, T.S., Stisen, A., Kjaergaard, M.B.: Analysis methods for extracting knowledge from large-scale WiFi monitoring to inform building facility planning. in Proc. IEEE Int. Conf. Pervasive Comput. Commun. 130–138 (2014). doi: https://doi.org/10.1109/PerCom.2014.6813953
Sarshar, H., Matwin, S.: Using classification in the preprocessing step on Wi-Fi data as an enabler of physical analytics. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Anaheim, CA, USA, 2016;944–949, doi: https://doi.org/10.1109/ICMLA.2016.0170.
Schauer, L., Werner, M., Marcus, P.: Estimating crowd densities and pedestrian flows using Wi-Fi and bluetooth, In Proc. 11th Int. Conf. Mobile Ubiquitous Syst. Comput. Netw. Serv. 171–177 (2014). DOI: https://doi.org/10.4108/icst.mobiquitous.2014.257870
Shang, R.X., Zhang, P.H., Zhong, M.H.: Investigation and analysis on evacuation behavior of large scale population in campus. Proc. Eng. 52, 302–308 (2013). https://doi.org/10.1016/j.proeng.2013.02.144
Sigg, S., et al.: Passive, device-free recognition on your mobile phone: Tools, features and a case study. In Proc. Mobile Ubiquitous Syst., Comput., Netw., Serv. 2014;435–446. doi: https://doi.org/10.1007/978-3-319-11569-6_34
Subramanian, G.H., Verma, A.: Crowd risk prediction in a spiritually motivated crowd. Saf. Sci. 155, 105877 (2022). https://doi.org/10.1016/j.ssci.2022.105877
Teixeira, T., Dublon, G., Savvides, A.: A survey of human-sensing: Methods for detecting presence, count, location, track, and identity. ACM Comput. Surv. 5, 59–69 (2010)
Wang, J., Tse, N.C.F., Chan, J.Y.C.: Wi-Fi based occupancy detection in a complex indoor space under discontinuous wireless communication: A robust filtering based on event-triggered updating. Build. Environ. 151(15), 228–239 (2019). https://doi.org/10.1016/j.buildenv.2019.01.043
Weng, W., Wang, J., Shen, L., Song, Y.: Review of analyses on crowd-gathering risk and its evaluation methods. J. Saf. Sci. Resil. 4(1), 93–107 (2023). https://doi.org/10.1016/j.jnlssr.2022.10.004
Yang, L.Z., Rao, P., Zhu, K.J., Liu, S.B., Zhan, X.: Observation study of pedestrian flow on staircases with different dimensions under normal and emergency conditions. Saf. Sci. 50, 1173–1179 (2012). https://doi.org/10.1016/j.ssci.2011.12.026
You, W., Liang, B., Shi, W., Wang, P., Zhang, X.: TaintMan: An ART-compatible dynamic taint analysis framework on unmodified and non-rooted android devices. IEEE Trans. Dependable Secure Comput. 17(1), 209–222 (2020). https://doi.org/10.1109/TDSC.2017.2740169
Zhang, J., Han, J., Xiang, L., Ng, D.W.K., Chen, M., Jo, M.: On the performance of LTE/Wi-Fi dual-mode uplink transmission: connection probability versus energy efficiency. IEEE Trans. Veh. Technol. 69(10), 11152–11168 (2020). https://doi.org/10.1109/TVT.2020.3007922
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Chellaswamy, C., Rao, C.S. & Geetha, T.S. Performance study of crowd flow in academic buildings of an institution. CCF Trans. Pervasive Comp. Interact. 5, 367–381 (2023). https://doi.org/10.1007/s42486-023-00134-9
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DOI: https://doi.org/10.1007/s42486-023-00134-9