A team-based multitask data acquisition scheme under time constraints in mobile crowd sensing

References

• Blondel, V., Esch, M., Chan, C., Clerot, F., Deville, P., Huens, E., Morlot, F., Smoreda, Z., & Ziemlicki, C. (2012). Data for development: The D4D challenge on mobile phone data. CoRR. abs/1210.0137.
   Google Scholar
• Cui, M., Han, D., Wang, J., Li, K. C., & Chang, C. C. (2020). Arfv: An efficient shared data auditing scheme supporting revocation for fog-assisted vehicular ad-hoc networks. IEEE Transactions on Vehicular Technology, 69(12), 15815–15827. https://doi.org/10.1109/TVT.25
   Web of Science ®Google Scholar
• Estrada, R., Mizouni, R., Otrok, H., Ouali, A., & Bentahar, J. (2020). A crowd-sensing framework for allocation of time-constrained and location-based tasks. IEEE Transactions on Services Computing, 13(5), 769–785. https://doi.org/10.1109/TSC.4629386
   Web of Science ®Google Scholar
• Gao, H., Liu, C. H., Tang, J., Yang, D., Hui, P., & Wang, W. (2019). Online quality-aware incentive mechanism for mobile crowd sensing with extra bonus. IEEE Transactions on Mobile Computing, 18(11), 2589–2603.
   Web of Science ®Google Scholar
• Guo, B., Liu, Y., Wang, L., Li, V. O. K., Lam, J. C. K., & Yu, Z. (2018). Task allocation in spatial crowdsourcing: Current state and future directions. IEEE Internet of Things Journal, 5(3), 1749–1764. https://doi.org/10.1109/JIOT.2018.2815982
   Web of Science ®Google Scholar
• Guo, B., Liu, Y., Wu, W., Yu, Z., & Han, Q. (2017). Activecrowd: A framework for optimized multitask allocation in mobile crowdsensing systems. IEEE Transactions on Human-Machine Systems, 47(3), 392–403. https://doi.org/10.1109/THMS.2016.2599489
   Web of Science ®Google Scholar
• Guo, B., Wang, Z., Yu, Z., Wang, Y., & Zhou, X. (2015). Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. Acm Computing Surveys, 48(1), 1–31. https://doi.org/10.1145/2794400
   Web of Science ®Google Scholar
• Hamrouni, A., Ghazzai, H., Alelyani, T., & Massoud, Y. (2020). A stochastic team formation approach for collaborative mobile crowdsourcing. IEEE. https://doi.org/10.1109/ICM48031.2019.9021910
   Google Scholar
• Hui-hui, C., Bin, G., & Zhi-wen, Y. (2020). Research on the method to collect high-quality crowdsourced data in open mode. Journal of Chinese Computer Systems, 41(1), 78–84.
   Google Scholar
• Jiang, W., Chen, J., Liu, X., Liu, Y., & Lv, S. (2021). Participant recruitment method aiming at service quality in mobile crowd sensing. Wireless Communications and Mobile Computing, 2021(3), 1–14. https://doi.org/10.1155/2021/6621659
   PubMedGoogle Scholar
• Jiang, Y., Cong, R., Shu, C., Yang, A., & Min, G. (2020). Federated learning based mobile crowd sensing with unreliable user data. 2020 IEEE 22nd international conference on high performance computing and communications, Fiji, December 14–16.
   Google Scholar
• Li, W., Fan, L., Sharif, K., & Yu, W. (2017). When user interest meets data quality: A novel user filter scheme for mobile crowd sensing. 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS), Shenzhen, China, December 15–17.
   Google Scholar
• Li, Y., Li, F., Yang, S., Wu, Y., Chen, H., Sharif, K., & Wang, Y. (2019). Mp-coopetition: Competitive and cooperative mechanism for multiple platforms in mobile crowd sensing. IEEE Transactions on Services Computing, 14(6), 1864–1876. https://doi.org/10.1109/TSC.2019.2916315.
   Web of Science ®Google Scholar
• Liang, W., Huang, W., Long, J., Zhang, K., Li, K., & Zhang, D. (2020). Deep reinforcement learning for resource protection and real-time detection in iot environment. IEEE Internet of Things Journal, 7(7), 6392–6401. https://doi.org/10.1109/JIoT.6488907
   Web of Science ®Google Scholar
• Liu, B., & Zou, Z. (2004). An experimental study on the efficiently individual quantity in cooperating team. Industrial Engineering And Management, 9(6), 89–94. https://doi.org/10.2174/0929866043478455
   Google Scholar
• Liu, Y., Guo, B., Wang, Y., Wu, W., Yu, Z., & Zhang, D. (2016). TaskMe: Multi-task allocation in mobile crowd sensing. 2016 ACM international joint conference on pervasive and ubiquitous computing, Heidelberg, Germany, September 12–16.
   Google Scholar
• Liu, Y., Guo, B., Wu, W.-L., Yu, Z.-W., & Zhang, D.-Q. (2017). Multitask-oriented participant selection in mobile crowd sensing. Jisuanji Xuebao/Chinese Journal of Computers, 40, 1872–1887. https://doi.org/10.11897/SP.J.1016.2017.0187
   Google Scholar
• Liu, Z., Liu, X., & Li, K. (2020). Deeper exercise monitoring for smart gym using fused rfid and CV data. 2020 IEEE 39th conference on computer communications (INFOCOM), Toronto, Canada, July 6–9.
   Google Scholar
• Luo, Z., Xu, J., Zhao, P., Yang, D., Xu, L., & Luo, J. (2021). Towards high quality mobile crowdsensing: Incentive mechanism design based on fine-grained ability reputation. Computer Communications, 180, 197–209. https://doi.org/10.1016/j.comcom.2021.09.026
   Web of Science ®Google Scholar
• Mahmud, R., Kotagiri, R., Buyya, R., Di Martino, B., Li, K., Yang, L., & Esposito, A. (2018). Internet of things (Technology, Communications and Computing) (Vol. 103, pp. 1–130). Springer.
   Google Scholar
• Mizouni, R., Otrok, H., Singh, S., Azzam, R., & Ouali, A. (2016). Grs: A group-based recruitment system for mobile crowd sensing. Journal of Network and Computer Applications, 72(sep.), 38–50. https://doi.org/10.1016/j.jnca.2016.06.015
   Google Scholar
• Niu, H., Liu, W., Wang, Z., & Sun, S. (2020). Multitask-oriented partipant selection in mobile crowd sensing. Journal of Chinese Computer Systems, 41(8), 1707–1712.
   Google Scholar
• Qiu, T., Zhao, A., Xia, F., Si, W., & Wu, D. O. (2017). Rose: Robustness strategy for scale-free wireless sensor networks. IEEE/ACM Transactions on Networking, 25(5), 2944–2959. https://doi.org/10.1109/TNET.2017.2713530
   Web of Science ®Google Scholar
• Sun, H., & Tao, D. (2019). Data-quality-aware participant selection mechanism for mobile crowdsensing. Wireless Sensor Networks.
   Google Scholar
• Tan, W., Zhao, L., Li, B., Xu, L. D., & Yang, Y. (2021). Multiple cooperative task allocation in group-oriented social mobile crowdsensing. IEEE Transactions on Services Computing, PP(99), 1–13. https://doi.org/10.1109/TSC.4629386
   Google Scholar
• Wang, J., Wang, L., Wang, Y., Zhang, D., & Kong, L. (2018). Task allocation in mobile crowd sensing: State-of-the-art and future opportunities. IEEE Internet of Things Journal, 5(5), 3747–3757. https://doi.org/10.1109/JIoT.6488907
   Web of Science ®Google Scholar
• Wang, J., Wang, Y., Zhang, D., Feng, W., & Qiu, Z. (2018). Multi-task allocation in mobile crowd sensing with individual task quality assurance. IEEE Transactions on Mobile Computing, 17(9), 2101–2113. https://doi.org/10.1109/TMC.7755
   Web of Science ®Google Scholar
• Wang, L., Yu, Z., Zhang, D., Guo, B., & Liu, C. H. (2019). Heterogeneous multi-task assignment in mobile crowdsensing using spatiotemporal correlation. IEEE Transactions on Mobile Computing, 18(1), 84–97. https://doi.org/10.1109/TMC.2018.2827375
   Web of Science ®Google Scholar
• Wang, W., Jiang, J., An, B., Jiang, Y., & Chen, B. (2017). Toward efficient team formation for crowdsourcing in noncooperative social networks. IEEE Transactions on Cybernetics, 47(12), 4208–4222. https://doi.org/10.1109/TCYB.2016.2602498
   PubMed Web of Science ®Google Scholar
• Yong, B., Wei, W., Li, K. C., Shen, J., & Damaeviius, R. (2020). Ensemble machine learning approaches for webshell detection in internet of things environments. Transactions on Emerging Telecommunications Technologies, (e4085). https://doi.org/10.1002/ett.4085.
   Google Scholar
• Zeng, Y., Sapienza, A., & Ferrara, E. (2019). The influence of social ties on performance in team-based online games. IEEE Transactions on Games, 13(4), 358–367. https://doi.org/10.1109/TG.2019.2923223.
   Web of Science ®Google Scholar
• Zhang, C., & Kamiyama, N. (2020). Data quality maximization for mobile crowdsensing. 2020 IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, Hungary, 20–24 April.
   Google Scholar
• Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., & Li, K. (2021). Applications of federated learning in smart cities: Recent advances, taxonomy, and open challenges. Connection Science 33(5). https://doi.org/10.1080/09540091.2021.1936455
   Google Scholar
• Zhu, X., Qu, W., Qiu, T., Zhao, L., Atiquzzaman, M., & Wu, D. O. (2020). Indoor intelligent fingerprint-based localization: Principles, approaches and challenges. IEEE Communications Surveys & Tutorials, 22(4), 2634–2657. https://doi.org/10.1109/COMST.9739
   Web of Science ®Google Scholar