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Joint pricing and task allocation for blockchain empowered crowd spectrum sensing

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Abstract

By fully utilizing the capability of the spreading intelligent terminals, crowd spectrum sensing is an efficient and cost-effective framework to realize large-scale and broadband spectrum sensing. However, traditional crowd sensing system relies on a centralized architecture, which not only face severe security and privacy issues, but also may not be able to attract enough users to participate in the sensing tasks due to the lack of effective incentive mechanisms and guaranteed rewards. In this paper, we propose a blockchain-based crowd spectrum sensing framework to achieve secure and privacy-preserving spectrum sensing with guaranteed rewards for participating users. Considering the constraint of the sensing task and the workload of users, the optimal pricing and sensing task allocation scheme under the minimum sensing task constraint is investigated by leveraging Stackelberg game model. We analyze the Nash equilibrium of the sub-games and derive the optimal pricing and sensing task allocation strategy, for both uniform pricing and non-uniform pricing schemes. Simulation results demonstrate the effectiveness of the proposed scheme and it is shown that the scheme can maximize the utility while ensuring the completion of the sensing tasks.

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References

  1. Zhou Y (2016) Feasibility research and suggestions of spectrum sharing. Telecomm Sci 32(5):146–151

    Google Scholar 

  2. Zhenhua D, Zhou S (2020) Dynamic adjustment mechanism for spectrum resource allocation in china. J Beijing Univ Posts Telecomm (Social Sciences Edition) 22(1):14–19

    Google Scholar 

  3. Li Z, Wang W, Wu Q (2020) Blockchain-based dynamic spectrum sharing for 5G and beyond wireless communications. In BlockSys

  4. Ma H, Zhao D, Yuan P (2014) Opportunities in mobile crowd sensing. IEEE Commun Mag 52(8):29–35

    Article  Google Scholar 

  5. Ni J, Zhang K, Yu Y, Lin X, Shen XS (2020) Providing task allocation and secure deduplication for mobile crowdsensing via fog computing. IEEE Trans Dependable Secure Comput 17(3):581–594

    Article  Google Scholar 

  6. Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39

    Article  Google Scholar 

  7. Huang J, Kong L, Dai H-N, Ding W, Cheng L, Chen G, Jin X, Zeng P (2020) Blockchain-based mobile crowd sensing in industrial systems. IEEE Trans Industr Inf 16(10):6553–6563

    Article  Google Scholar 

  8. Wei L, Jing W, Long C, Lin Y-B (2019) The convergence of IoE and blockchain: Security challenges. IT Professional 21(5):26–32

    Article  Google Scholar 

  9. Wei L, Jing W, Long C (2020) A blockchain-based hybrid incentive model for crowdsensing. Electronics 9(2):215

    Article  Google Scholar 

  10. Liu Y, Li H, Guan X, Yuan K, Zhao G, Duan J (2018) Review of incentive mechanism for mobile crowd sensing. J Chongqing Univ Posts Telecommun (Natural Science Edition) 30(2):147–158

  11. Liu J, Huang S, Wang W, Li D, Deng X (2021) An incentive mechanism based on endowment effect facing social welfare in crowdsensing. Peer Peer Netw Appl 14:3929–3945

    Article  Google Scholar 

  12. Khan SN, Loukil F, Ghedira C, Benkhelifa E, Bani-Hani AI (2021) Blockchain smart contracts: Applications, challenges, and future trends. Peer Peer Netw Appl 14:2901–2925

    Article  Google Scholar 

  13. Xinyi Y, Yi Z, He Y (2018) Technical characteristics and model of blockchain. In 2018 10th International Conference on Communication Software and Networks (ICCSN) pp. 562–566

  14. Liu D, Ni J, Huang C, Lin X, Shen XS (2020) Secure and efficient distributed network provenance for iot: A blockchain-based approach. IEEE Internet Things J 7(8):7564–7574

    Article  Google Scholar 

  15. Lv X, Zhu Q (2018) A multi-task crowd cooperative spectrum sensing algorithm. J Signal Process 34(4):487–493

    Google Scholar 

  16. Tian S, Zhao S, Zhu Q (2018) Cooperative spectrum sensing algorithm based on bayesian game. J Nanjing Univ Posts Telecommun (Natural Science) 38(2):29–34

  17. Li J, Feng J, Kang S, Guo Y (2013) Ssim: An incentive mechanism based on social selfishness for cooperative spectrum sensing. In 2013 8th International Conference on Communications and Networking in China (CHINACOM), 969–972

  18. Li X, Zhu Q (2018) Social incentive mechanism based multi-user sensing time optimization in co-operative spectrum sensing with mobile crowd sensing. Sensors 18(1):250

    Article  Google Scholar 

  19. Nie J, Luo J, Xiong Z, Niyato D, Wang P (2019) A stackelberg game approach toward socially-aware incentive mechanisms for mobile crowdsensing. IEEE Trans Wireless Commun 18(1):724–738

    Article  Google Scholar 

  20. Zhang X, Zhu Q (2020) A multi-task cooperative spectrum sensing algorithm based on stackelberg game. J Signal Process 36(1):77–83

    MathSciNet  Google Scholar 

  21. Yang D, Xue G, Fang X, Tang J (2016) Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones. IEEE/ACM Trans Netw 24(3):1732–1744

    Article  Google Scholar 

  22. Chen B, Zhang B, Yu J-L, Chen Y, Han Z (2017) An indirect reciprocity based incentive framework for cooperative spectrum sensing. In 2017 IEEE International Conference on Communications (ICC) pp. 1–6

  23. Hajian G, Ghahfarokhi BS, Vasfi MA, Ladani BT (2021) Privacy, trust, and secure rewarding in mobile crowd-sensing based spectrum monitoring. J Ambient Intell Humaniz Comput pp. 1–21

  24. Li M, Weng J, Yang A, Lu W, Zhang Y, Hou L, Liu J-N, Xiang Y (2018) Deng RH (2018) Crowdbc: A blockchain-based decentralized framework for crowdsourcing. IEEE Trans Parallel Distrib Syst 30(6):1251–1266

    Article  Google Scholar 

  25. Wei X, Yan Y, Jiang W, Shen J, Qiu X (2019) A blockchain based mobile crowdsensing market. China Communications 16(6):31–41

    Article  Google Scholar 

  26. Huang J, Kong L, Kong L, Liu Z, Liu Z, Chen G (2018) Blockchain-based crowd-sensing system. In 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN) pp. 234–235

  27. Xiaolong X, Liu Q, Zhang X, Zhang J, Qi L, Dou W (2019) A blockchain-powered crowdsourcing method with privacy preservation in mobile environment. IEEE Trans Comput Social Syst 6(6):1407–1419

    Article  Google Scholar 

  28. Kadadha M, Otrok H, Mizouni R, Singh S, Ouali A (2020) Sensechain: A blockchain-based crowdsensing framework for multiple requesters and multiple workers. Futur Gener Comput Syst 105:650–664

    Article  Google Scholar 

  29. Wang Jingzhong, Li M, He Y, Li H, Xiao K, Wang C (2018) A blockchain based privacy-preserving incentive mechanism in crowdsensing applications. IEEE Access 6:17545–17556

    Article  Google Scholar 

  30. Peng T, Liu J, Chen J, Wang G (2020) A privacy-preserving crowdsensing system with muti-blockchain. In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) pp. 1944–1949

  31. Jia B, Zhou T, Li W, Liu Z, Zhang J (2018) A blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Sensors 18(11):3894

    Article  Google Scholar 

  32. Zou S, Xi J, Wang H, Guoai X (2020) Crowdblps: A blockchain-based location-privacy-preserving mobile crowdsensing system. IEEE Trans Industr Inf 16(6):4206–4218

    Article  Google Scholar 

  33. Cao B, Xia S, Han J, Li Y (2020) A distributed game methodology for crowdsensing in uncertain wireless scenario. IEEE Trans Mob Comput 19(1):15–28

    Article  Google Scholar 

  34. Bacci G, Sanguinetti L, Luise M (2015) Understanding game theory via wireless power control [lecture notes]. IEEE Signal Process Mag 32(4):132–137

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Key R&D Program of China under Grant 2020YFB1005900, the National Natural Science Foundation of China No. 62001220, the Natural Science Foundation of Jiangsu Province BK20200440, the Future Network Scientific Research Fund Project FNSRFP-2021-YB-03, and the Fundamental Research Funds for the Central Universities No. 1004-YAH20016, No. NT2020009.

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Wenbin Chen, Wei Wang, Zuguang Li & Qihui Wu

  2. Memorial University of Newfoundland, St. Johns, Canada

    Qiang Ye

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  1. Wenbin Chen
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  2. Wei Wang
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  3. Zuguang Li
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  4. Qiang Ye
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  5. Qihui Wu
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Correspondence to Wei Wang.

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Chen, W., Wang, W., Li, Z. et al. Joint pricing and task allocation for blockchain empowered crowd spectrum sensing. Peer-to-Peer Netw. Appl. 15, 783–792 (2022). https://doi.org/10.1007/s12083-021-01283-3

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  • DOI: https://doi.org/10.1007/s12083-021-01283-3

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