Task Allocation Method of Mobile Crowdsensing Based on Group Collaboration

WU Dapeng; GUAN Peng; ZHANG Puning; YANG Zhigang; WANG Ruyan

doi:10.11999/JEIT221046

Volume 45 Issue 12

Dec. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(12): 4308-4316

WU Dapeng, GUAN Peng, ZHANG Puning, YANG Zhigang, WANG Ruyan. Task Allocation Method of Mobile Crowdsensing Based on Group Collaboration[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4308-4316. doi: 10.11999/JEIT221046

Citation:

WU Dapeng, GUAN Peng, ZHANG Puning, YANG Zhigang, WANG Ruyan. Task Allocation Method of Mobile Crowdsensing Based on Group Collaboration[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4308-4316. doi: 10.11999/JEIT221046

Citation:

PDF( 2452 KB)

Task Allocation Method of Mobile Crowdsensing Based on Group Collaboration

doi: 10.11999/JEIT221046

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing 400065, China
3.
Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61901071, 61871062, 61771082, U20A20157), The Science and Natural Science Foundation of Chongqing, China (cstc2020jcyj-zdxmX0024), The University Innovation Research Group of Chongqing (CXQT20017), The Program for Innovation Team Building at Institutions of Higher Education in Chongqing (CXTDX201601020)

Received Date: 2022-08-09
Rev Recd Date: 2023-04-20

Available Online: 2023-04-23

Publish Date: 2023-12-26

Abstract

Abstract

The temporal and spatial constraints of spatio-temporal coverage tasks make it difficult to utilize the traditional single-participant model. Therefore, a task allocation method based on group collaboration in mobile crowdsensing is proposed to replace the traditional single participant mode with group mode. A task allocation framework for hierarchical group collaboration and a preference-aware social group generation method is proposed, In addition, social groups are generated by introducing social relationships to improve the task completion rate. A task-group matching method for utility optimization is proposed, and the network flow theory is used to perform group-task matching to ensure the maximum utility of the platform. Simulation results show that the proposed method can improve the task completion rate and platform utility.
- Mobile crowdsensing,
- Task assignment,
- Group collaboration,
- Platform utility

FullText(HTML)

References(16)

References

[1]	TAN Wenan, ZHAO Lu, LI Bo, et al. Multiple cooperative task allocation in group-oriented social mobile crowdsensing[J]. IEEE Transactions on Services Computing, 2022, 15(6): 3387–3401. doi: 10.1109/TSC.2021.3086097
[2]	WANG Jiangtao, WANG Feng, WANG Yasha, et al. HyTasker: Hybrid task allocation in mobile crowd sensing[J]. IEEE Transactions on Mobile Computing, 2020, 19(3): 598–611. doi: 10.1109/TMC.2019.2898950
[3]	WANG Zhibo, ZHAO Jing, HU Jiahui, et al. Towards personalized task-oriented worker recruitment in mobile crowdsensing[J]. IEEE Transactions on Mobile Computing, 2021, 20(5): 2080–2093. doi: 10.1109/TMC.2020.2973990
[4]	TAO Xi and SONG Wei. Profit-oriented task allocation for mobile crowdsensing with worker dynamics: Cooperative offline solution and predictive online solution[J]. IEEE Transactions on Mobile Computing, 2021, 20(8): 2637–2653. doi: 10.1109/TMC.2020.2983688
[5]	XU Jia, RAO Zhengqiang, XU Lijie, et al. Incentive mechanism for multiple cooperative tasks with compatible users in mobile crowd sensing via online communities[J]. IEEE Transactions on Mobile Computing, 2020, 19(7): 1618–1633. doi: 10.1109/TMC.2019.2911512
[6]	ZHAO Yan, ZHENG Kai, YIN Hongzhi, et al. Preference-aware task assignment in spatial crowdsourcing: From individuals to groups[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(7): 3461–3477. doi: 10.1109/TKDE.2020.3021028
[7]	LEI Yu, WANG Zhitao, LI Wenjie, et al. Social attentive deep Q-networks for recommender systems[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(5): 2443–2457. doi: 10.1109/TKDE.2020.3012346
[8]	ZHAO Yiming, SONG Wei, and HAN Zhu. Social-aware data dissemination via device-to-device communications: Fusing social and mobile networks with incentive constraints[J]. IEEE Transactions on Services Computing, 2019, 12(3): 489–502. doi: 10.1109/TSC.2016.2599160
[9]	WANG Wendong, GAO Hui, LIU C H, et al. Credible and energy-aware participant selection with limited task budget for mobile crowd sensing[J]. Ad Hoc Networks, 2016, 43: 56–70. doi: 10.1016/j.adhoc.2016.02.007
[10]	WU Fan, YANG Shuo, ZHENG Zhenzhe, et al. Fine-grained user profiling for personalized task matching in mobile crowdsensing[J]. IEEE Transactions on Mobile Computing, 2021, 20(10): 2961–2976. doi: 10.1109/TMC.2020.2993963
[11]	OGUNDELE T J, CHOW C Y, and ZHANG Jiadong. SoCaST: Exploiting social, categorical and spatio-temporal preferences for personalized event recommendations[C]. The 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), Exeter, UK, 2017: 38–45.
[12]	LI Ji, CAI Zhipeng, YAN Mingyuan, et al. Using crowdsourced data in location-based social networks to explore influence maximization[C]. The 35th Annual IEEE International Conference on Computer Communications, San Francisco, USA, 2016: 1–9.
[13]	WANG Liang, YANG Dingqi, YU Zhiwen, et al. Acceptance-aware mobile crowdsourcing worker recruitment in social networks[J]. IEEE Transactions on Mobile Computing, 2023, 22(2): 634–646. doi: 10.1109/TMC.2021.3090764
[14]	ZHAO Lu, TAN Wenan, LI Bo, et al. Multiple cooperative task assignment on reliability-oriented social crowdsourcing[J]. IEEE Transactions on Services Computing, 2022, 15(6): 3402–3416. doi: 10.1109/TSC.2021.3103636
[15]	XU Chenghao and SONG Wei. Efficient data uploading for mobile crowdsensing via team collaborating and matching[J]. IEEE Transactions on Green Communications and Networking, 2022, 6(1): 645–654. doi: 10.1109/TGCN.2021.3109740
[16]	LUO Shuyun, SUN Yongmei, WEN Zhenyu, et al. C2: Truthful incentive mechanism for multiple cooperative tasks in mobile cloud[C]. 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, 2016: 1–6.