Skip to main content
SpringerLink
Log in

CSSA-based collaborative optimization recommendation of users in mobile crowdsensing

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

With the large-scale popularization of mobile terminals, crowd sensing technology has gradually replaced the existing static sensors with its efficient and low-cost advantages as an emerging data collection method. How to quickly allocate the sensing task to the optimal execution user under the premise of ensuring the perceived quality and reducing the cost is the focus of the research. In this regard, this paper proposes a Crowd sensing Sparrow Search Algorithm (CSSA) collaborative optimization recommendation method that combines fitness priority, collaboration, and intelligent optimization algorithms, and uses it for task allocation problems. Firstly, the concept of fitness is proposed to calculate the location, power, equipment and reputation of the perceived user, and analyze the matching degree of the user to the task. Secondly, according to the different fitness, the user is divided into explorers and followers, and the two cooperate to complete the perception task. Thirdly, in the process of solving the optimal task allocation scheme, CSSA intelligent optimization algorithm is used to simulate the process of users completing tasks, and the selected user results can be obtained after limited iterations. Through the comparative experiments of the proposed algorithm and other optimization algorithms in the same environment, the results show that it has higher performance in solving the task allocation problem.

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

References

  1. Guo B, Wang Z, Yu Z et al (2015) Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput Surv 48(1):1–31

    Article  Google Scholar 

  2. Ali A, Qureshi MA, Shiraz M et al (2021) Mobile crowd sensing based dynamic traffic efficiency framework for urban traffic congestion control. Sustain Comput: Inform Syst 32:100608

    Google Scholar 

  3. Boubiche DE, Imran M, Maqsood A et al (2019) Mobile crowd sensing–Taxonomy, applications, challenges, and solutions. Comput Hum Behav 101:352–370

    Article  Google Scholar 

  4. Liu J, Cao H, Li Q et al (2018) A large-scale concurrent data anonymous batch verification scheme for mobile healthcare crowd sensing. IEEE Internet Things J 6(2):1321–1330

    Article  Google Scholar 

  5. Sun J, Ma H (2014) Heterogeneous-belief based incentive schemes for crowd sensing in mobile social networks. J Netw Comput Appl 42:189–196

    Article  Google Scholar 

  6. Gao H, Liu CH, Tang J et al (2018) Online quality-aware incentive mechanism for mobile crowd sensing with extra bonus. IEEE Trans Mob Comput 18(11):2589–2603

    Article  Google Scholar 

  7. Wen Y, Shi J, Zhang Q et al (2014) Quality-driven auction-based incentive mechanism for mobile crowd sensing. IEEE Trans Veh Technol 64(9):4203–4214

    Article  Google Scholar 

  8. Gao X, Huang H, Liu C et al (2020) Quality inference based task assignment in mobile crowdsensing. IEEE Trans Knowl Data Eng 33(10):3410–3423

    Article  Google Scholar 

  9. Tu J, Cheng P, Chen L (2019) Quality-assured synchronized task assignment in crowdsourcing. IEEE Trans Knowl Data Eng 33(3):1156–1168

    Google Scholar 

  10. Song S, Liu Z, Li Z et al (2020) Coverage-oriented task assignment for mobile crowdsensing. IEEE Internet Things J 7(8):7407–7418

    Article  Google Scholar 

  11. Chen J, Yang J (2019) Maximizing coverage quality with budget constrained in mobile crowd-sensing network for environmental monitoring applications. Sensors 19(10):2399

    Article  Google Scholar 

  12. El-Ashmawi WH, Ali AF (2020) A modified salp swarm algorithm for task assignment problem. Appl Soft Comput 94:106445

    Article  Google Scholar 

  13. Bhattacharjee S, Ghosh N, Shah VK et al (2018) QnQ: Quality and quantity based unified approach for secure and trustworthy mobile crowdsensing. IEEE Trans Mob Comput 19(1):200–216

    Article  Google Scholar 

  14. Gao H, Liu C H, Tian Y et al (2017) Ensuring high-quality data collection for mobile crowd sensing. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, p 1–6

  15. Alsayasneh M, Amer-Yahia S, Gaussier E et al (2017) Personalized and diverse task composition in crowdsourcing. IEEE Trans Knowl Data Eng 30(1):128–141

    Article  Google Scholar 

  16. Xue J, Shen B (2020) A novel swarm intelligence optimization approach: Sparrow search algorithm. Syst Sci Control Eng 8(1):22–34

    Article  Google Scholar 

  17. Krishna MB, Lorenz P (2021) Collaborative participatory crowd sensing using reputation and reliability with expectation maximization for IoT networks. In: ICC 2021-IEEE International Conference on Communications. IEEE, p 1–6

  18. Dande B, Chang CY, Liao WH et al (2022) MSQAC: Maximizing the surveillance quality of area coverage in wireless sensor networks. IEEE Sens J 22(6):6150–6163 (3–5)

  19. Khan F, Rehman AU, Zheng J et al (2019) Mobile crowdsensing: a survey on privacy-preservation, task management, assignment models, and incentives mechanisms. Futur Gener Comput Syst 100:456–472

    Article  Google Scholar 

  20. Lin Y, Cai Z, Wang X et al (2021) Multi-round incentive mechanism for cold start-enabled mobile crowdsensing. IEEE Trans Veh Technol 70(1):993–1007

    Article  Google Scholar 

  21. Yang G, Wang B, He X et al (2021) Competition-congestion-aware stable worker-task matching in mobile crowd sensing. IEEE Trans Netw Serv Manage 18(3):3719–3732

    Article  Google Scholar 

  22. Gong W, Zhang B, Li C (2017) Location-based online task scheduling in mobile crowdsensing. In: IEEE Global Communications Conference. IEEE, p 1–6

  23. Li Z, Xu Z, Chen X (2019) Location-related online multitask allocation algorithm for mobile swarm intelligence perception. Comput Sci 46(6):102–106

  24. Xiao M, Wu J, Huang L et al (2017) Online task assignment for crowdsensing in predictable mobile social networks. IEEE Trans Mob Comput 16(8):2306–2320

    Article  Google Scholar 

  25. Wang E, Yang Y, Wu J et al (2017) An efficient prediction-based user recruitment for mobile crowdsensing. IEEE Trans Mob Comput 17(1):16–28

    Article  Google Scholar 

  26. Tsai HC (2021) A corrected and improved symbiotic organisms search algorithm for continuous optimization. Expert Syst Appl 177:114981

    Article  Google Scholar 

  27. Xiaofeng Z, Xiuying W (2019) Review on the research of grey wolf optimization algorithm. Comput Sci 46(3):30–38

    Google Scholar 

  28. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  29. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  30. Gong W, Zhang B, Li C (2018) Location-based online task assignment and path planning for mobile crowdsensing. IEEE Trans Veh Technol 68(2):1772–1783

    Article  Google Scholar 

  31. Azzam R, Mizouni R, Otrok H et al (2016) GRS: A group-based recruitment system for mobile crowd sensing. J Netw Comput Appl 72:38–50

    Article  Google Scholar 

  32. Yang Z, Zhou C, Yuan S (2019) Task allocation based on discrete cuckoo search algorithm in mobile swarm intelligence sensing system.Comput Appl 39(9):2778–2783

  33. Jiang W, Zhang W, Chen P et al (2021) Based on the number-sensitive task allocation method in IWOA group intelligence perception [J/OL]. Electronics 1(16):0372–2112

    Google Scholar 

  34. Anwar T, Uma V (2019) CD-SPM: Cross-domain book recommendation using sequential pattern mining and rule mining. J King Saud Univ Comput Inf Sci

  35. Anwar T, Uma V (2019) Mrec-crm: Movie recommendation based on collaborative filtering and rule mining approach. In: 2019 international conference on Smart Structures and Systems (ICSSS). IEEE, p 1–5

  36. Anwar T, Uma V, Srivastava G (2021) Rec-cfsvd++: Implementing recommendation system using collaborative filtering and singular value decomposition (svd)++. Int J Inf Technol Decis Mak 20(04):1075–1093

    Article  Google Scholar 

  37. Yin B, Li J, Wei X (2021) Rational task assignment and path planning based on location and task characteristics in mobile crowdsensing. IEEE Trans Comput Soc Syst

  38. Yang S, Wu F, Tang S et al (2017) On designing data quality-aware truth estimation and surplus sharing method for mobile crowdsensing. IEEE J Sel Areas Commun 35(4):832–847

    Article  Google Scholar 

  39. Wang W, Duan X, Sun W et al (2021) Research on mobility prediction in 5G and beyond for vertical industries. IEEE, p 379–383

  40. Liu S, Zheng Z, Wu F et al (2017) Context-aware data quality estimation in mobile crowdsensing. In: IEEE Conference on Computer Communications. IEEE, p 1–9

Download references

Acknowledgements

All the authors listed have approved the manuscript that is enclosed.

Funding

This present research work was supported by the National Natural Science Foundation of China (61403109, 61202458), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20112303120007) and the Natural Science Foundation of Heilongjiang Province (LH2020F034).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

    Jian Wang & Shuai Hao

  2. College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China

    Guosheng Zhao

Authors
  1. Jian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guosheng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jian Wang, Shuai Hao and Guosheng Zhao wrote the manuscript together.

Corresponding author

Correspondence to Jian Wang.

Ethics declarations

Ethical approval and consent to participate

I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

Human and animal ethics

Not applicable.

Consent for publication

We would like to submit the manuscript entitled “CSSA-Based Collaborative Optimization Recommendation of Users in Mobile Crowdsensing”, which we wish to be considered for publication in “Peer-to-Peer Networking and Applications”.

Competing interests

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, J., Hao, S. & Zhao, G. CSSA-based collaborative optimization recommendation of users in mobile crowdsensing. Peer-to-Peer Netw. Appl. 16, 803–817 (2023). https://doi.org/10.1007/s12083-022-01444-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-022-01444-y

Keywords

Search

Navigation