Peer-to-Peer Transfers for Crowd Monitoring - A Reality Check

  • Christin GrobaEmail author
  • Alexander SchillEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1231)


Peer-to-peer transfers allow for sharing crowd monitoring data despite the loss of network connectivity. However, limited insight into real-world deployment contexts can let the protocol design go astray - particularly, if a certain nature of participant behaviour and connectivity changes is assumed. This paper focuses on the delivery of crowd monitoring data. It puts a protocol out for a reality check that switches to peer-to-peer (p2p) communication when the infrastructure network connection is lost. The evaluation at an annual indoor fair asked visitors to make their phones visible to peers, run the protocol, and share crowd monitoring data. The results show that most of the participants formed a large radio cluster throughout the event. This made p2p networking only possible and enabled a more robust upload of crowd monitoring data. However, dynamic switching between infrastructure network and p2p communication also increased the volatility of the system, calling for future optimizations. The presented measurement results provide further insights into these details.


Crowd monitoring Peer-to-peer Bluetooth Android Real-world evaluation 


  1. 1.
    Blanke, U., Tröster, G., Franke, T., Lukowicz, P.: Capturing crowd dynamics at large scale events using participatory GPS-localization. In: IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings (ISSNIP), pp. 21–24. IEEE (2014).
  2. 2.
    Castagno, P., Mancuso, V., Sereno, M., Marsan, M.A.: Why your smartphone doesn’t work in very crowded environments. In: IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–9, June 2017.
  3. 3.
    Danielis, P., Kouyoumdjieva, S.T., Karlsson, G.: Urbancount: mobile crowd counting in urban environments. In: 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 640–648, October 2017.
  4. 4.
    Groba, C., Springer, T.: Exploring data forwarding with bluetooth for participatory crowd monitoring. In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 71–76, March 2019.
  5. 5.
    Kannan, P.G., Venkatagiri, S.P., Chan, M.C., Ananda, A.L., Peh, L.S.: Low cost crowd counting using audio tones. In: Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, SenSys 2012, pp. 155–168. Association for Computing Machinery, New York (2012).
  6. 6.
    Kjægaard, M.B., Wirz, M., Roggen, D., Tröster, G.: Mobile sensing of pedestrian flocks in indoor environments using wifi signals. In: 2012 IEEE International Conference on Pervasive Computing and Communications, pp. 95–102, March 2012.
  7. 7.
    Kluge, T., Groba, C., Springer, T.: Trilateration, fingerprinting, and centroid: taking indoor positioning with bluetooth LE to the wild. In: 21st International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM) (WoWMoM 2020). Cork, Ireland, June 2020. AcceptedGoogle Scholar
  8. 8.
    Loomba, R., de Frein, R., Jennings, B.: Selecting energy efficient cluster-head trajectories for collaborative mobile sensing. In: IEEE Global Communications Conference (GLOBECOM), pp. 1–7 (2015).
  9. 9.
    Wirz, M., Franke, T., Roggen, D., Mitleton-Kelly, E., Lukowicz, P., Tröster, G.: Probing crowd density through smartphones in city-scale mass gatherings. EPJ Data Sci. 2(1), 1–24 (2013). Scholar
  10. 10.
    Wirz, M., Schläpfer, P., Kjundefinedrgaard, M.B., Roggen, D., Feese, S., Tröster, G.: Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks. LBSN 2011, p. 17–24. Association for Computing Machinery, New York (2011).
  11. 11.
    Zhang, J., Guo, H., Liu, J.: Energy-aware task offloading for ultra-dense edge computing. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 720–727 (2018).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Chair of Computer NetworksTechnische Universität DresdenDresdenGermany

Personalised recommendations