User Fine-Grained Reliability and Truth Estimate Model on Mobile Crowdsensing

doi:10.13190/j.jbupt.2021-217

Journal of Beijing University of Posts and Telecommunications ›› 2022, Vol. 45 ›› Issue (4): 70-76.doi: 10.13190/j.jbupt.2021-217

• Special Topics on Wireless Sensor Networks • Previous Articles Next Articles

User Fine-Grained Reliability and Truth Estimate Model on Mobile Crowdsensing

LIU Likun^1,2, QIU Tie^1,2, XU Tianyi^1,2, CHEN Ning^1,2, WAN Zhiguo³

1. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
2. Tianjin Key Laboratory of Advanced Networking, Tianjin 300350, China;
3. Zhijiang Laboratory, Zhejiang 311121, China

Received:2021-09-26 Online:2022-08-28 Published:2022-06-26

Abstract

Abstract: To improve perceived data quality in mobile crowdsensing, a method for estimating the truth value of crowdsensing tasks based on user fine-grained reliability is proposed, whichselect high-quality data through user modeling. First, the real-time reliability of users is evaluated according to the instantaneous factors that affect their task execution. Then, information entropy is introduced to measure the user's reputation distribution, including the user's overall reputation distribution and the reputation distribution under different tasks. Next, based on user reliability, an effective truth estimation method is designed to predict task truth. Experimental results show that the proposed model can effectively evaluate the reliability of users in multi-type tasks and improve the accuracy of task truth estimation.

Key words: mobile crowdsensing, reliability model, truth estimate

CLC Number:

TP393

LIU Likun, QIU Tie, XU Tianyi, CHEN Ning, WAN Zhiguo. User Fine-Grained Reliability and Truth Estimate Model on Mobile Crowdsensing[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(4): 70-76.

References

[1] 杨朔, 吴帆, 陈贵海. 移动群智感知网络中信息量最大化的用户选择方法研究[J]. 计算机学报, 2020, 43(3):409-422. YANG S, WU F, CHEN G H. On designing most infor-mative user selection methods for mobile crowdsensing[J]. Chinese Journal of Computers, 2020, 43(3):409-422.
[2] BHATTACHARJEE S, GHOSH N, SHAH V K, et al. QnQ:quality and quantity based unified approach for secure and trustworthy mobile crowdsensing[J]. IEEE Transactions on Mobile Computing, 2020, 19(1):200-216.
[3] QIU T, LIU J, SI W S, et al. Robustness optimization scheme with multi-population co-evolution for scale-free wireless sensor networks[J]. IEEE/ACM Transactions on Networking, 2019, 27(3):1028-1042.
[4] QIU T, ZHAO A Y, XIA F, et al. ROSE:robustness strategy for scale-free wireless sensor networks[J]. IEEE/ACM Transactions on Networking, 2017, 25(5):2944-2959.
[5] YANG J L, WANG J S, TAY W P. Using social network information in community-based Bayesian truth discovery[J]. IEEE Transactions on Signal and Information Processing Over Networks, 2019, 5(3):525-537.
[6] AUGUSTIN A, VENANZI M, ROGERS A, et al. Bayesian aggregation of categorical distributions with applications in crowdsourcing[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne:International Joint Conferences on Artificial Intelligence Organization, 2017:1411-1417.
[7] DU Y, SUN Y, HUANG H, et al. Bayesian co-clustering truth discovery for mobile crowd sensing systems[J]. IEEE Transactions on Industrial Informatics, 2020, 16(2):1045-1057.
[8] WANG X, JIA R H, TIAN X H, et al. Location-aware crowdsensing:dynamic task assignment and truth inference[J]. IEEE Transactions on Mobile Computing, 2020, 19(2):362-375.
[9] GAO X F, HUANG H W, LIU C L, et al. Quality inference based task assignment in mobile crowdsensing[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(10):3410-3423.
[10] YANG S, WU F, TANG S J, et al. On designing data quality-aware truth estimation and surplus sharing method for mobile crowdsensing[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(4):832-847.
[11] WU F, YANG S, ZHENG Z Z, et al. Fine-grained user profiling for personalized task matching in mobile crowdsensing[J]. IEEE Transactions on Mobile Computing, 2021, 20(10):2961-2976.
[12] FIANDRINO C, ANJOMSHOA F, KANTARCI B, et al. Sociability-driven framework for data acquisition in mobile crowdsensing over fog computing platforms for smart cities[J]. IEEE Transactions on Sustainable Computing, 2017, 2(4):345-358.
[13] LIU C H, ZHANG B, SU X, et al. Energy-aware participant selection for smartphone-enabled mobile crowd sensing[J]. IEEE Systems Journal, 2017, 11(3):1435-1446.
[14] WU D P, LIU Q R, WANG H G, et al. Socially aware energy-efficient mobile edge collaboration for video distribution[J]. IEEE Transactions on Multimedia, 2017, 19(10):2197-2209.
[15] WU D P, LI H P, WANG R Y. User characteristic aware participant selection for mobile crowdsensing[J/OL]. Sensors (Basel, Switzerland), 2018, 18(11):3959[2021-09-21]. https//doi.org/10.3390/s18113959.
[16] TAO D, GAO R P, SUN H B. Sensing-gain constrained participant selection mechanism for mobile crowdsensing[J]. Personal and Ubiquitous Computing, 2020(5):1-15.
[17] LI Q, LI Y, GAO J, et al. Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation[C]//Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. New York:ACM, 2014:1187-1198.

User Fine-Grained Reliability and Truth Estimate Model on Mobile Crowdsensing

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

Comments

[1]	SHI Rui, FENG Hua-min, YANG Yang, YUAN Feng, LIU Biao. Mobile Crowdsensing Scheme with Strong Privacy-Preserving [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(5): 114-120.
[2]	WU Zhen, MENG Luo-ming. Software Reliability Model on Failure Correlation and Maintenance Policy [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2007, 30(6): 94-98.