A Survey on Truth Discovery in Crowdsensing

Main Article Content

Yadong Peng
Tianxi Wang
Qing Lang

Keywords

crowdsensing, data quality, truth discovery, privacy protection

Abstract

In recent years, the rapid proliferation of smartphones and wearable devices has significantly propelled the development of crowdsensing. As a prerequisite for ensuring the secure operation of crowdsensing services, data quality has emerged as a critical issue that demands urgent resolution. This paper first introduces the main components and system workflows of crowdsensing, followed by an account of the basic concepts, principles, and key research focuses of truth discovery methods in crowdsensing. By categorizing truth discovery approaches based on modeling paradigms, this study systematically reviews the current research landscape, analyzes and compares existing methods, and constructs a comprehensive framework for their evaluation. Finally, it synthesizes the findings and identifies future challenges for truth discovery in crowdsensing, aligning with the evolving application demands of the field.

Abstract 0 | PDF Downloads 0

References

  • An, J., Liang, D., Gui, X., Yang, H., Gui, R., & He, X. (2019). Crowdsensing quality control and grading evaluation based on a two-consensus blockchain. IEEE Internet of Things Journal, 6(3), 4711-4718. https://doi.org/10.1109/JIOT.2018.2883835
  • Cai, X., Zhou, L., Li, F., Fu, Y., Zhao, P., Li, C., & Yu, F. R. (2023). An incentive mechanism for vehicular crowdsensing with security protection and data quality assurance. IEEE Transactions on Vehicular Technology, 72(8), 9984-9998. https://doi.org/10.1109/TVT.2023.3262800
  • 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 and Tutorials, 21(3), 2419-2465. https://doi.org/10.1109/COMST.2019.2914030
  • Cheng, Y., Ma, J., Liu, Z., Li, Z., Wu, Y., Dong, C., & Li, R. (2023). A privacy-preserving and reputation-based truth discovery framework in mobile crowdsensing. IEEE Transactions on Dependable and Secure Computing, 20(6), 5293-5311. https://doi.org/10.1109/TDSC.2023.3276976
  • Cheng, Y., Ma, J., Liu, Z., Wu, Y., Wei, K., & Dong, C. (2023). A lightweight privacy preservation scheme with efficient reputation management for mobile crowdsensing in vehicular networks. IEEE Transactions on Dependable and Secure Computing, 20(3), 1771-1788. https://doi.org/10.1109/TDSC.2022.3163752
  • Dai, C., Wang, X., Liu, K., Qi, D., Lin, W., & Zhou, P. (2021). Stable task assignment for mobile crowdsensing with budget constraint. IEEE Transactions on Mobile Computing, 20(12), 3439-3452. https://doi.org/10.1109/TMC.2020.3000234
  • Ding, S., He, X., & Wang, J. (2017). Multiobjective optimization model for service node selection based on a tradeoff between quality of service and resource consumption in mobile crowd sensing. IEEE Internet of Things Journal, 4(1), 258-268. https://doi.org/10.1109/JIOT.2017.2647740
  • Du, Y., Sun, Y. E., Huang, H., Huang, L., Xu, H., Bao, Y., & Guo, H. (2020). Bayesian co-clustering truth discovery for mobile crowd sensing systems. IEEE Transactions on Industrial Informatics, 16(2), 1045-1057. https://doi.org/10.1109/TII.2019.2896287
  • Feng, C., Wang, W., Tian, Y., Que, X., & Gong, X. (2017). Estimate air quality based on mobile crowd sensing and big data [Paper presentation]. 18th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, WoWMoM 2017 - Conference, Macau, China.
  • Gao, J., Fu, S., Luo, Y., & Xie, T. (2020). Location privacy-preserving truth discovery in mobile crowd sensing [Paper presentation]. Proceedings - International Conference on Computer Communications and Networks, ICCCN, Honolulu, HI, USA.
  • Gong, X., & Shroff, N. B. (2019). Truthful mobile crowdsensing for strategic users with private data quality. IEEE/ACM Transactions on Networking, 27(5), 1959-1972. https://doi.org/10.1109/TNET.2019.2934026
  • Guo, B., Yu, Z., Zhou, X., & Zhang, D. (2014). From participatory sensing to mobile crowd sensing [Paper presentation]. 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014, Budapest, Hungary.
  • Hu, C., Li, Z., Xu, Y., Zhang, C., Liu, X., He, D., & Zhu, L. (2024). Multiround efficient and secure truth discovery in mobile crowdsensing systems. IEEE Internet of Things Journal, 11(10), 17210-17222. https://doi.org/10.1109/JIOT.2024.3359757
  • Kang, Y., Liu, A., Xiong, N. N., Zhang, S., Wang, T., & Dong, M. (2024). DTD: An intelligent data and bid dual truth discovery scheme for MCS in IIoT. IEEE Internet of Things Journal, 11(2), 2507-2519. https://doi.org/10.1109/JIOT.2023.3292920
  • Kim, J. W., Edemacu, K., & Jang, B. (2022). Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey. Journal of Network and Computer Applications, 200, Article 103315. https://doi.org/10.1016/J.JNCA.2021.103315
  • Li, Q., Li, Y., Gao, J., Su, L., Zhao, B., Demirbas, M., Fan, W., & Han, J. (2014). A confidence-aware approach for truth discovery on long-tail data. Proceedings of the VLDB Endowment, 8(4), 425-436. https://doi.org/10.14778/2735496.2735505
  • Li, Q., Li, Y., Gao, J., Zhao, B., Fan, W., & Han, J. (2014). Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation [Paper presentation]. Proceedings of the ACM SIGMOD International Conference on Management of Data, New York, NY, United States.
  • Li, Y., Gao, J., Meng, C., Li, Q., Su, L., Zhao, B., Fan, W., & Han, J. (2016). A survey on truth discovery. ACM SIGKDD Explorations Newsletter, 17(2), 1-16. https://doi.org/10.1145/2897350.2897352
  • Liu, F., Zhu, B., Yuan, S., Li, J., & Xue, K. (2021). Privacy-preserving truth discovery for sparse data in mobile crowdsensing systems [Paper presentation]. Proceedings - IEEE Global Communications Conference, GLOBECOM, Madrid, Spain.
  • Liu, J., Shao, J., Sheng, M., Xu, Y., Taleb, T., & Shiratori, N. (2024). Mobile crowdsensing ecosystem with combinatorial multi-armed bandit-based dynamic truth discovery. IEEE Transactions on Mobile Computing, 23(12), 13095-13113. https://doi.org/10.1109/TMC.2024.3428542
  • Mehdi, M., Schwager, D., Pryss, R., Schlee, W., Reichert, M., & Hauck, F. J. (2019). Towards automated smart mobile crowdsensing for tinnitus research [Paper presentation]. Proceedings - IEEE Symposium on Computer-Based Medical Systems, Cordoba, Spain.
  • Miao, C., Jiang, W., Su, L., Li, Y., Guo, S., Qin, Z., Xiao, H., Gao, J., & Ren, K. (2015). Cloud-enabled privacy-preserving truth discovery in crowd sensing systems [Paper presentation]. SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, New York, NY, United States.
  • Peng, T., Zhong, W., Wang, G., Zhang, S., Luo, E., & Wang, T. (2024). Spatiotemporal-aware privacy-preserving task matching in mobile crowdsensing. IEEE Internet of Things Journal, 11(2), 2394-2406. https://doi.org/10.1109/JIOT.2023.3292284
  • Perez, A. J., & Zeadally, S. (2022). Secure and privacy-preserving crowdsensing using smart contracts: Issues and solutions. Computer Science Review, 43, Article 100450. https://doi.org/10.1016/J.COSREV.2021.100450
  • Restuccia, F., Ghosh, N., Bhattacharjee, S., Das, S. K., & Melodia, T. (2017). Quality of information in mobile crowdsensing: Survey and research challenges. ACM Transactions on Sensor Networks, 13(4), 1-43. https://doi.org/10.1145/3139256
  • Singh, V., Chander, D., Chhaparia, U., & Raman, B. (2018). Safestreet: An automated road anomaly detection and early-warning system using mobile crowdsensing [Paper presentation]. 2018 10th International Conference on Communication Systems and Networks, COMSNETS 2018, Bengaluru, India.
  • Wang, L., Yu, Z., Wu, K., Yang, D., Wang, E., Wang, T., Mei, Y., & Guo, B. (2023). Towards robust task assignment in mobile crowdsensing systems. IEEE Transactions on Mobile Computing, 22(7), 4297-4313. https://doi.org/10.1109/TMC.2022.3151190
  • Wang, P., Li, Z., Guo, B., Long, S., Guo, S., & Cao, J. (2024). A UAV-assisted truth discovery approach with incentive mechanism design in mobile crowd sensing. IEEE/ACM Transactions on Networking, 32(2), 1738-1752. https://doi.org/10.1109/TNET.2023.3331059
  • Wang, Y., Yan, Z., Feng, W., & Liu, S. (2020). Privacy protection in mobile crowd sensing: A survey. World Wide Web, 23(1), 421-452. https://doi.org/10.1007/S11280-019-00745-2
  • Wang, Z., Cao, Y., Jiang, K., Zhou, H., Kang, J., Zhuang, Y., Tian, D., & Leung, V. C. M. (2024). When crowdsensing meets smart cities: A comprehensive survey and new perspectives. IEEE Communications Surveys and Tutorials, 27(2), 1101-1151. https://doi.org/10.1109/COMST.2024.3400121
  • Wu, H., Wang, L., Cheng, K., Yang, D., Tang, J., & Xue, G. (2022). Privacy-enhanced and practical truth discovery in two-server mobile crowdsensing. IEEE Transactions on Network Science and Engineering, 9(3), 1740-1755. https://doi.org/10.1109/TNSE.2022.3151228
  • Xu, Y., Xiao, M., Zhu, Y., Wu, J., Zhang, S., & Zhou, J. (2024). Aoi-guaranteed incentive mechanism for mobile crowdsensing with freshness concerns. IEEE Transactions on Mobile Computing, 23(5), 4107-4125. https://doi.org/10.1109/TMC.2023.3285779
  • Yan, L., & Yang, S. (2021). Trust-aware truth discovery with long-term vehicle reputation for internet of vehicles crowdsensing [Paper presentation]. 2021 International Wireless Communications and Mobile Computing, IWCMC 2021, Harbin City, China.
  • Zhang, H., & Li, M. (2022). Multi-round data poisoning attack and defense against truth discovery in crowdsensing systems [Paper presentation]. Proceedings - IEEE International Conference on Mobile Data Management, Paphos, Cyprus.
  • Zhao, B., & Han, J. (2012, 2012). A probabilistic model for estimating real-valued truth from conflicting sources [Paper presentation]. Proc. of QDB, Istanbul, Turkey.
  • Zhao, B., Rubinstein, B. I. P., Gemmell, J., & Han, J. (2012). A bayesian approach to discovering truth from conflicting sources for data integration. Proceedings of the VLDB Endowment, 5(6), 550-561. https://doi.org/10.14778/2168651.2168656
  • Zheng, Y., Duan, H., Yuan, X., & Wang, C. (2020). Privacy-aware and efficient mobile crowdsensing with truth discovery. IEEE Transactions on Dependable and Secure Computing, 17(1), 121-133. https://doi.org/10.1109/TDSC.2017.2753245
  • Zhou, T., Cai, Z., & Su, J. (2022). Discovering truth in mobile crowdsensing with differential location privacy [Paper presentation]. Proceedings - IEEE Global Communications Conference, GLOBECOM, Rio de Janeiro, Brazil.