ABSTRACT
Crowd analysis and monitoring in large congregations is an important problem related to public safety and planning. Researchers have long been using image and video for effective crowd analysis. However, with the advent of more sophisticated technologies, crowd monitoring has been attempted using GPS, RFID as well as Bluetooth based systems. Indoor crowd monitoring in public buildings, such as, airport, museum, theaters, etc. has not been widely studied with regards to systems that are not integrated with the indoor environment, such as Wi-Fi access points. In this paper, we propose a non-invasive Wi-Fi based approach for indoor crowd analysis which works by passively monitoring the probe packets generated by smartphones. Since smartphones, and other smart devices, nowadays can be unequivocally associated with a human user, counting unique MAC addresses of smartphones can generate a fairly close estimate of number of users in the indoor space and their movement patterns. In this paper, we have developed the MiamiMapper system, from COTS hardware and software, for crowd analysis in indoor environment using passive monitoring of probe packets emitted by wireless devices. Our system can localize users and thereby detect crowd movement using probe counts with an average accuracy of 7.28 meters.
- Yuvraj Agarwal, Bharathan Balaji, Rajesh Gupta, Jacob Lyles, Michael Wei, and Thomas Weng. 2010. Occupancy-driven Energy Management for Smart Building Automation. In Proceedings of the 2Nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building (BuildSys '10). ACM, New York, NY, USA, 1--6. https://doi.org/10.1145/1878431.1878433Google ScholarDigital Library
- A. R. Al-Ali, F. A. Aloul, N. R. Aji, A. A. Al-Zarouni, and N. H. Fakhro. 2008. Mobile RFID Tracking System. In 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications. 1--4. https://doi.org/ 10.1109/ICTTA.2008.4530117Google Scholar
- Alfa.com. 2019. Alfa Wireless Adapter (AWUS036H), Wireless-G with SMA Adapter (2dBi or 5dBi Antenna). http://www.alfa.com.tw/products_show.php? pc=34&ps=92. (2019).Google Scholar
- Marco V Barbera, Alessandro Epasto, Alessandro Mei, Vasile C Perta, and Julinda Stefa. 2013. Signals from the crowd: uncovering social relationships through smartphone probes. In Proceedings of the 2013 conference on Internet measurement conference. ACM, 265--276.Google ScholarDigital Library
- F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, and C. Ratti. 2011. Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome. IEEE Transactions on Intelligent Transportation Systems 12, 1 (March 2011), 141--151. https://doi. org/10.1109/TITS.2010.2074196Google ScholarDigital Library
- G. De Angelis, G. Baruffa, and S. Cacopardi. 2013. GNSS/Cellular Hybrid Positioning System for Mobile Users in Urban Scenarios. IEEE Transactions on Intelligent Transportation Systems 14, 1 (March 2013), 313--321. https://doi.org/10.1109/ TITS.2012.2215855Google ScholarDigital Library
- P. C. Deepesh, Rashmita Rath, Akshay Tiwary, Vikram N. Rao, and N. Kanakalata. 2016. Experiences with Using iBeacons for Indoor Positioning. In Proceedings of the 9th India Software Engineering Conference (ISEC '16). ACM, New York, NY, USA, 184--189. https://doi.org/10.1145/2856636.2856654Google Scholar
- P. Fuxjaeger, S. Ruehrup, T. Paulin, and B. Rainer. 2016. Towards Privacy- Preserving Wi-Fi Monitoring for Road Traffic Analysis. IEEE Intelligent Transportation Systems Magazine 8, 3 (Fall 2016), 63--74. https://doi.org/10.1109/MITS. 2016.2573341Google ScholarCross Ref
- Shaogang Gong, Chen Change Loy, and Tao Xiang. 2011. Security and Surveillance. Springer London, London, 455--472. https://doi.org/10.1007/978-0--85729--997- 0_23Google Scholar
- Y. Gu, A. Lo, and I. Niemegeers. 2009. A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys Tutorials 11, 1 (First 2009), 13--32. https://doi.org/10.1109/SURV.2009.090103Google ScholarDigital Library
- Nik Harris. 2019. Probemon. https://github.com/nikharris0/probemon. (2019).Google Scholar
- S. He and S. . G. Chan. 2016. Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons. IEEE Communications Surveys Tutorials 18, 1 (Firstquarter 2016), 466--490. https://doi.org/10.1109/COMST.2015.2464084Google ScholarDigital Library
- Hande Hong, Girisha Durrel De Silva, and Mun Choon Chan. 2018. CrowdProbe: Non-invasive Crowd Monitoring with Wi-Fi Probe. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3, Article 115 (Sept. 2018), 23 pages. https: //doi.org/10.1145/3264925Google ScholarDigital Library
- Xueheng Hu, Lixing Song, Dirk Van Bruggen, and Aaron Striegel. 2015. Is there wifi yet?: How aggressive probe requests deteriorate energy and throughput. In Proceedings of the 2015 Internet Measurement Conference. ACM, 317--323.Google ScholarDigital Library
- Taeyu Im and Pradipta De. 2016. User-Assisted OCR on Outdoor Images for Approximate Positioning. In Information Science and Applications (ICISA) 2016, Kuinam J. Kim and Nikolai Joukov (Eds.). Springer Singapore, Singapore, 1419-- 1429.Google Scholar
- Cisco Inc. 2019. Cisco Wireless Location Appliance. http: //www.cisco.com/c/en/us/products/collateral/wireless/wireless-locationappliance/ product_data_sheet0900aecd80293728.html. (2019).Google Scholar
- Tarun Kulshrestha, Divya Saxena, Rajdeep Niyogi, Vaskar Raychoudhury, and Manoj Misra. 2017. SmartITS: Smartphone-based identification and tracking using seamless indoor-outdoor localization. Journal of Network and Computer Applications 98 (2017), 97 -- 113. https://doi.org/10.1016/j.jnca.2017.09.003Google ScholarDigital Library
- Libelium. 2019. Libelium, Smartphone Detection. http://www.libelium.com/ products/meshlium/Smartphone-detection/. (2019).Google Scholar
- H. Liu, H. Darabi, P. Banerjee, and J. Liu. 2007. Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 37, 6 (Nov 2007), 1067--1080. https: //doi.org/10.1109/TSMCC.2007.905750Google ScholarDigital Library
- Halgurd S Maghdid, Ihsan Alshahib Lami, Kayhan Zrar Ghafoor, and Jaime Lloret. 2016. Seamless outdoors-indoors localization solutions on smartphones: implementation and challenges. ACM Computing Surveys (CSUR) 48, 4 (2016), 53.Google ScholarDigital Library
- T. Mantoro, A. D. Jaafar, M. F. M. Aris, and M. A. Ayu. 2011. HajjLocator: A Hajj pilgrimage tracking framework in crowded ubiquitous environment. In 2011 International Conference on Multimedia Computing and Systems. 1--6. https: //doi.org/10.1109/ICMCS.2011.5945629Google ScholarCross Ref
- R. O. Mitchell, H. Rashid, F. Dawood, and A. AlKhalidi. 2013. Hajj crowd management and navigation system: People tracking and location based services via integrated mobile and RFID systems. In 2013 International Conference on Computer Applications Technology (ICCAT). 1--7. https://doi.org/10.1109/ICCAT.2013.6522008Google ScholarCross Ref
- M. Mohandes. 2011. Pilgrim tracking and identification using the mobile phone. In 2011 IEEE 15th International Symposium on Consumer Electronics (ISCE). 196--199. https://doi.org/10.1109/ISCE.2011.5973812Google ScholarCross Ref
- L. M. Ni, D. Zhang, and M. R. Souryal. 2011. RFID-based localization and tracking technologies. IEEE Wireless Communications 18, 2 (April 2011), 45--51. https: //doi.org/10.1109/MWC.2011.5751295Google ScholarCross Ref
- Janice Partyka. 2012. A Look at Small Indoor Location Competitors. https://www. gpsworld.com/wirelesslook-small-indoor-location-competitors-13229/. (2012).Google Scholar
- W. Pattanusorn, I. Nilkhamhang, S. Kittipiyakul, K. Ekkachai, and A. Takahashi. 2016. Passenger estimation system using Wi-Fi probe request. In 2016 7th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES). 67--72. https://doi.org/10.1109/ICTEmSys.2016.7467124Google Scholar
- V. Raychoudhury, S. Shrivastav, S. S. Sandha, and J. Cao. 2015. CROWD-PAN-360: Crowdsourcing Based Context-Aware Panoramic Map Generation for Smartphone Users. IEEE Transactions on Parallel and Distributed Systems 26, 8 (Aug 2015), 2208--2219. https://doi.org/10.1109/TPDS.2014.2345067Google ScholarCross Ref
- Liqing Ren, Xiaojiang Chen, Binbin Xie, Zhanyong Tang, Tianzhang Xing, Chen Liu, Weike Nie, and Dingyi Fang. 2016. DE2: localization based on the rotating RSS using a single beacon. Wireless Networks 22, 2 (01 Feb 2016), 703--721. https://doi.org/10.1007/s11276-015-0998--9Google Scholar
- J. C. Silveira Jacques Junior, S. R. Musse, and C. R. Jung. 2010. Crowd Analysis Using Computer Vision Techniques. IEEE Signal Processing Magazine 27, 5 (Sep. 2010), 66--77. https://doi.org/10.1109/MSP.2010.937394Google Scholar
- Mathias Versichele, Tijs Neutens, Matthias Delafontaine, and Nico Van deWeghe. 2012. The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the Ghent Festivities. Applied Geography 32, 2 (2012), 208 -- 220. https://doi.org/10.1016/j.apgeog.2011.05.011Google ScholarCross Ref
- Wei Wang, Raj Joshi, Aditya Kulkarni, Wai Kay Leong, and Ben Leong. 2013. Feasibility Study of Mobile Phone WiFi Detection in Aerial Search and Rescue Operations. In Proceedings of the 4th Asia-PacificWorkshop on Systems (APSys '13). ACM, New York, NY, USA, Article 7, 6 pages. https://doi.org/10.1145/2500727. 2500729Google ScholarDigital Library
- M. Wirz, T. Franke, D. Roggen, E. Mitleton-Kelly, P. Lukowicz, and G. Tröster. 2012. Inferring Crowd Conditions from Pedestrians' Location Traces for Real- Time Crowd Monitoring during City-Scale Mass Gatherings. In 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. 367--372. https://doi.org/10.1109/WETICE.2012.26Google ScholarDigital Library
- Jiang Xiao, Zimu Zhou, Youwen Yi, and Lionel M. Ni. 2016. A Survey on Wireless Indoor Localization from the Device Perspective. ACM Comput. Surv. 49, 2, Article 25 (June 2016), 31 pages. https://doi.org/10.1145/2933232Google Scholar
- C. Yang and H. Shao. 2015. WiFi-based indoor positioning. IEEE Communications Magazine 53, 3 (March 2015), 150--157. https://doi.org/10.1109/MCOM.2015. 7060497Google ScholarDigital Library
Index Terms
- MiamiMapper: Crowd Analysis using Active and Passive Indoor Localization through Wi-Fi Probe Monitoring
Recommendations
MIMO CSI-based Super-resolution AoA Estimation for Wi-Fi Indoor Localization
ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and ComputingIndoor localization technology has always been a research hotspot in industry and academia. Indoor localization research using channel state information (CSI) of Wi-Fi signals has also received more and more attention. The existing Angle of Arrival (AoA)...
A Wi-Fi Positioning Method Considering Radio Attenuation of Human Body
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersAs it is generally difficult to utilize GPS positioning indoors, methods such as Wi-Fi positioning and PDR positioning are proposed. In particular, in addition to being able to estimate the absolute position, the Wi-Fi positioning has attracted ...
Smartphone based indoor localization using stable access points
Workshops ICDCN '18: Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and NetworkingIndoor localization, based on Wi-Fi signals, is becoming a popular approach for providing location based services in indoor environment. The challenging task of accurately finding the position of a device depends on prior efforts of fingerprinting. ...
Comments