Skip to main content
SpringerLink
Log in

SigSense: Mobile Crowdsensing Based Incentive Aware Geospatial Signal Monitoring for Base Station Installation Recommendation Using Mixed Reality Game

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

SigSense, a mobile crowdsensing-based geospatial video game, has been proposed to survey live signal strength using smartphones.It provides attractive incentives to the contributers. Live data collected as a survey through this game is used to recommend locations for installing the base stations to improve the signal quality using the Greedy Algorithm. A large plot of land is considered a large matrix. We have recursively divided the land into smaller submatrices. Then signal strength survey of each submatrix is performed through Mobile Crowd Sensing using SigSense. The recommendation system advices the locations for installing the network base stations for improving signal strength. An incentive is provided to a player based on the game's rules, making it a win–win situation for both the player and the network service provider. The unique feature of this game is that it can be played even in an area where is low mobile network coverage. A player’s details are hidden from other players through unique masked ids and mixed reality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Pandey, S. R., Tran, N. H., Bennis, M., Tun, Y. K., Manzoor, A., & Hong, C. S. (2020). A crowdsourcing framework for on-device federated learning. IEEE Transactions on Wireless Communications, 19(5), 3241–3256.

    Article  Google Scholar 

  2. Li, Z., Liu, H., & Wang, R. (2019). Service benefit aware multi-task assignment strategy for mobile crowd sensing. Sensors, 19(21), 4666.

    Article  Google Scholar 

  3. Saadatmand, S., & Kanhere, S. S. (2019). MRA: A modified reverse auction based framework for incentive mechanisms in mobile crowdsensing systems. Computer Communications, 145, 137–145.

    Article  Google Scholar 

  4. De, D. (2019). Mobile cloud computing: Architectures, algorithms and applications. Chapman and Hall/CRC.

  5. Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019). A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities. IEEE Communications Surveys & Tutorials, 21(3), 2419–2465.

    Article  Google Scholar 

  6. Koukoumidis, E., Peh, L. S., & Martonosi, M. R. (2011). Signalguru: Leveraging mobile phones for collaborative traffic signal schedule advisory. In Proceedings of the 9th international conference on mobile systems, applications, and services (pp. 127–140).

  7. Costa, C., Laoudias, C., Zeinalipour-Yazti, D., & Gunopulos, D. (2011). SmartTrace: Finding similar trajectories in smartphone networks without disclosing the traces. In 2011 IEEE 27th international conference on data engineering (pp. 1288–1291). IEEE.

  8. Wu, C., Yang, Z., & Liu, Y. (2014). Smartphones based crowdsourcing for indoor localization. IEEE Transactions on Mobile Computing, 14(2), 444–457.

    Article  Google Scholar 

  9. Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., & Eriksson, J. (2009). Vtrack: Accurate, energy-aware road traffic delay estimation using mobile phones. In Proceedings of the 7th ACM conference on embedded networked sensor systems (pp. 85–98).

  10. Yang, M., Zhu, T., Liang, K., Zhou, W., & Deng, R. H. (2019). A blockchain-based location privacy-preserving crowdsensing system. Future Generation Computer Systems, 94, 408–418.

    Article  Google Scholar 

  11. Zhang, X., Yang, Z., Sun, W., Liu, Y., Tang, S., Xing, K., & Mao, X. (2015). Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys & Tutorials, 18(1), 54–67.

    Article  Google Scholar 

  12. Tao, D., Wu, T. Y., Zhu, S., & Guizani, M. (2020). Privacy protection-based incentive mechanism for Mobile Crowdsensing. Computer Communications, 156, 201–210.

    Article  Google Scholar 

  13. Bekele, M. K., Pierdicca, R., Frontoni, E., Malinverni, E. S., & Gain, J. (2018). A survey of augmented, virtual, and mixed reality for cultural heritage. Journal on Computing and Cultural Heritage (JOCCH), 11(2), 1–36.

    Article  Google Scholar 

  14. Flavián, C., Ibáñez-Sánchez, S., & Orús, C. (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience. Journal of Business Research, 100, 547–560.

    Article  Google Scholar 

  15. Broll, G., & Benford, S. (2005). Seamful design for location-based mobile games. In International conference on entertainment computing (pp. 155–166). Springer, Berlin, Heidelberg.

  16. Barkhuus, L., Chalmers, M., Tennent, P., Hall, M., Bell, M., Sherwood, S., & Brown, B. (2005). Picking pockets on the lawn: the development of tactics and strategies in a mobile game. In International conference on ubiquitous computing (pp. 358–374). Springer, Berlin, Heidelberg.

  17. Drozd, A., Benford, S., Tandavanitj, N., Wright, M., & Chamberlain, A. (2006). Hitchers: Designing for cellular positioning. In International conference on ubiquitous computing (pp. 279–296). Springer, Berlin, Heidelberg.

  18. Bell, M., Reeves, S., Brown, B., Sherwood, S., MacMillan, D., Ferguson, J., & Chalmers, M. (2009). Eyespy: Supporting navigation through play. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 123–132).

  19. Khan, F., Rehman, A. U., Zheng, J., Jan, M. A., & Alam, M. (2019). Mobile crowdsensing: A survey on privacy-preservation, task management, assignment models, and incentives mechanisms. Future Generation Computer Systems, 100, 456–472.

    Article  Google Scholar 

  20. Ongbwa, B. A. E., Kora, A. D., Mbemba, H., & Amelete, S. (2018). Objective approach of 3G/4G networks indoor RF coverage assessment. In 2018 4th International conference on frontiers of signal processing (ICFSP) (pp. 17–23). IEEE.

  21. Trogh, J., Plets, D., Surewaard, E., Spiessens, M., Versichele, M., Martens, L., & Joseph, W. (2019). Outdoor location tracking of mobile devices in cellular networks. EURASIP Journal on Wireless Communications and Networking, 2019(1), 1–18.

    Article  Google Scholar 

  22. Prasetya, D. A., Nguyen, P. T., Faizullin, R., Iswanto, I., & Armay, E. F. (2020). Resolving the shortest path problem using the haversine algorithm. Journal of Critical Reviews, 7(1), 62–64.

    Google Scholar 

  23. Geetha, K., & Kannan, A. (2019). An efficient information system for providing location based services in network environments. Wireless Personal Communications, 109(4), 2377–2398.

    Article  Google Scholar 

  24. Wei, X., Hong, H., Daniel, D. Z., & Wei, W. (2017). Emergency event web information acquisition using crowd web sensors. Wireless Personal Communications, 95(3), 2393–2411.

Download references

Author information

Authors and Affiliations

  1. Centre of Mobile Cloud Computing, Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Haringhata, Nadia, West Bengal, 741249, India

    Aakashjit Bhattacharya & Debashis De

Authors
  1. Aakashjit Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debashis De
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Debashis De.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhattacharya, A., De, D. SigSense: Mobile Crowdsensing Based Incentive Aware Geospatial Signal Monitoring for Base Station Installation Recommendation Using Mixed Reality Game. Wireless Pers Commun 123, 2863–2894 (2022). https://doi.org/10.1007/s11277-021-09267-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09267-5

Keywords

Search

Navigation