Skip to main content
SpringerLink
Log in

A trust-based minimum cost and quality aware data collection scheme in P2P network

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

Abstract

Peer-to-Peer (P2P) network can be a promising big data application platform. However, efficient data collection in network still faces a huge challenge in security. In this paper, we utilize the idea of machine learning to select trusted data reporter to collect data. The data collection optimization is translated into how to maximize data coverage and minimize the cost under given budget in malicious network, which has rarely been considered in previous studies. Then a Trust-based Minimum Cost Quality Aware (TMCQA) data collection scheme is proposed to perform data collection optimization. The data collection in TMCQA scheme has the following innovations. (1) A trust evaluation mechanism utilizing the idea of machine learning is established to evaluate the trust of the data reporter. Besides, different from the previous strategy, data reporter is taken as the basic unit of data collection instead of data sample which makes the TMCQA scheme is more practical in the P2P network.(2) An optimized data reporter selection strategy is proposed to select optimized reporters based on the three key evaluation indices to improve data collection performance, which are: (a) The trust value of the data reporter. The more credible the reporter, the higher the quality of the collected data. (b) The data coverage ratio for covering the interesting area of the sensing task. (c) The cost of data collection. The data reporter used to have lower cost will be selected to reduce cost. Finally, we verify the validity of the TMCQA scheme proposed in this paper through various experiments. Comparing to Contribution-based with Trust Value Scheme (CNTVS), Random Data Reporter Selection Scheme (RDRSS), and No Time Decay Scheme (TNTDS), with the same budget, the QoS can be improved by 32.21%, 49.39%, 23.68% respectively. The performance of the TMCQA scheme in a malicious P2P network is significantly better than previous strategies.

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
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): A vision, architectural elements, and future directions. Futur Gener Comput Syst 29(7):1645–1660

    Google Scholar 

  2. Saad S, Traore I, Ghorbani A, Sayed B, Zhao D, Lu W, ... & Hakimian, P (2011) Detecting P2P botnets through network behavior analysis and machine learning. In 2011 Ninth annual international conference on privacy, security and trust IEEE:174–180

  3. Liu Y, Ma M, Liu X, Xiong N, Liu A, Zhu Y (2020) Design and Analysis of Probing Route to Defense Sink-hole Attacks for Internet of Things Security. IEEE Trans Netw Sci Eng 7(1):356–372

  4. Teng H, Liu Y, Liu A, Xiong NN, Cai Z, Wang T, Liu X (2019) A novel code data dissemination scheme for internet of things through mobile vehicle of smart cities. Futur Gener Comput Syst 94:351–367

    Google Scholar 

  5. Liu Y, Liu A, Liu X, Huang X (2019) A statistical approach to participant selection in location-based social networks for offline event marketing. Inf Sci 480:90–108

    Google Scholar 

  6. Kelly SDT, Suryadevara NK, Mukhopadhyay SC (2013) Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sensors J 13(10):3846–3853

    Google Scholar 

  7. Chung K, Park RC (2016) P2P cloud network services for IoT based disaster situations information. Peer-to-Peer Networking and Applications 9(3):566–577

    Google Scholar 

  8. Sanchez L, Muñoz L, Galache JA, Sotres P, Santana JR, Gutierrez V et al (2014) SmartSantander: IoT experimentation over a smart city testbed. Comput Netw 61:217–238

    Google Scholar 

  9. Schulz P, Matthe M, Klessig H, Simsek M, Fettweis G, Ansari J et al (2017) Latency critical IoT applications in 5G: perspective on the design of radio interface and network architecture. IEEE Commun Mag 55(2):70–78

    Google Scholar 

  10. Ren H, Li H, Dai Y, Yang K, Lin X (2018) Querying in internet of things with privacy preserving: challenges, solutions and opportunities. IEEE Netw 32(6):144–151

    Google Scholar 

  11. Liu X, Zhao M, Liu A, Wong KKL (2020) Adjusting forwarder nodes and duty cycle using packet aggregation routing for body sensor networks. Information Fusion 53:183–195

    Google Scholar 

  12. Chen M, Wang T, Ota K, Dong M, Zhao M, Liu A (2019) Intelligent resource allocation Management for Vehicles Network: an A3C learning approach. Comput Commun 151:485–494

    Google Scholar 

  13. Wang J, Wang F, Wang Y, Wang L, Qiu Z, Zhang D et al (2019) HyTasker: hybrid task allocation in Mobile crowd sensing. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2019.2898950

  14. Zhang G, Wang T, Wang G, Liu A, Jia W (2018) Detection of hidden data attacks combined fog computing and trust evaluation method in sensor-cloud system. Practice and Experience, Concurrency and Computation. https://doi.org/10.1002/cpe.5109

  15. Liu F, Tang G, Li Y, Cai Z, Zhang X, Zhou T (2019) A survey on edge computing systems and tools. Proc IEEE 107(8):1537–1562

    Google Scholar 

  16. Wu M, He S, Zhang Y, Chen J, Sun Y, Liu Y, Zhang J, Poor V (2019) A tensor-based framework for studying eigenvector multicentrality in multilayer networks. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 116(31):15407–15413

    Google Scholar 

  17. Hao M., Li H., Luo X., Xu G., Yang H., & Liu S. (2019). Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Transactions on Industrial Informatics DOI: https://doi.org/10.1109/TII.2019.2945367

  18. Ren Y, Liu W, Wang T, Li X, Xiong N, Liu A (2019) A collaboration platform for effective task and data reporter selection in crowdsourcing network. IEEE Access 7:19238–19257

    Google Scholar 

  19. Liu Q, Tian Y, Wu J, Peng T, Wang G (2019) Enabling verifiable and dynamic ranked search over outsourced data. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2019.2922177

  20. Liu X, Liu Y, Zhang N, Wu W, Liu A (2019) Optimizing trajectory of unmanned aerial vehicles for efficient data acquisition: A matrix completion approach. IEEE Internet Things J 6(2):1829–1840

    Google Scholar 

  21. Martí IG, Rodríguez LE, Benedito M, Trilles S, Beltrán A, Díaz L, Huerta J (2012) Mobile application for noise pollution monitoring through gamification techniques. In International Conference on Entertainment Computing Springer, Berlin, Heidelberg, pp 562–571

    Google Scholar 

  22. Zhang N, Yang P, Ren J, Chen D, Yu L, Shen X (2018) Synergy of big data and 5g wireless networks: opportunities, approaches, and challenges. IEEE Wirel Commun 25(1):12–18

    Google Scholar 

  23. Liu Y, Liu A, Zhang N, Liu X, Ma M, Hu Y (2019) DDC: dynamic duty cycle for improving delay and energy efficiency in wireless sensor networks. J Netw Comput Appl 131:16–27

    Google Scholar 

  24. Thiagarajan A, Ravindranath L, LaCurts K, Madden S, Balakrishnan H, Toledo S, Eriksson J (2009) VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. In proceedings of the 7th ACM conference on embedded networked sensor systems: 85-98.

  25. Yu H, Shen Z, Miao C, An B (2012) Challenges and opportunities for trust management in crowdsourcing. In proceedings of the the 2012 IEEE/WIC/ACM international joint conferences on web intelligence and intelligent agent technology IEEE computer society, 02: 486-493.

  26. Hu L., Liu A., Xie M., & Wang T. (2019). UAVs joint vehicles as data mules for fast codes dissemination for edge networking in smart city. Peer-to-Peer Networking and Applications:1–25

  27. Liu Y, Liu A, Xiong NN, Wang T, Gui W (2019) Content propagation for content-centric networking systems from location-based social networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49(10):1946–1960

    Google Scholar 

  28. Li H, Yang Y, Dai Y, Yu S, Xiang Y (2017) Achieving secure and efficient dynamic searchable symmetric encryption over medical cloud data. IEEE Transactions on Cloud Computing. https://doi.org/10.1109/TCC.2017.2769645

  29. Zhang X, Xue G, Yu R, Yang D, Tang J (2015) Keep your promise: mechanism design against free-riding and false-reporting in crowdsourcing. IEEE Internet Things J 2(6):562–572

    Google Scholar 

  30. Zhang Y, Van der Schaar M (2012) Reputation-based incentive protocols in crowdsourcing applications. In 2012 proceedings IEEE INFOCOM: 2140-2148.

  31. Xu G, Li H, Dai Y, Yang K, Lin X (2019) Enabling efficient and geometric range query with access control over encrypted spatial data. IEEE Transactions on Information Forensics and Security 14(4):870–885

    Google Scholar 

  32. Liu Q, Hou P, Wang G, Peng T, Zhang S (2019) Intelligent route planning on large road networks with efficiency and privacy. Journal of Parallel and Distributed Computing 133:93–106

    Google Scholar 

  33. Tan J, Liu W, Wang T, Zhao M, Liu A, Zhang S (2020) A high-accurate content popularity prediction computational modelling for Mobile edge computing by using matrix completion technology. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.3871

  34. Liu Y, Liu A, Liu X, Ma M (2019) A trust-based active detection for cyber-physical security in industrial environments. IEEE Transactions on Industrial Informatics 15(12):6593–6603

    Google Scholar 

  35. Chung K, Park RC (2016) P2P cloud network services for IoT based disaster situations information. Peer-to-Peer Networking and Applications 9(3):566–577

    Google Scholar 

  36. Xu G, Li H, Liu S, Yang K, Lin X (2020) VerifyNet: secure and verifiable federated learning. IEEE Transactions on Information Forensics and Security 15(1):911–926

    Google Scholar 

  37. Huang M, Liu W, Wang T, Liu A, Zhang S (2019) A cloud-MEC collaborative task offloading scheme with service orchestration. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2019.2952767

  38. Behrend TS, Sharek DJ, Meade AW, Wiebe EN (2011) The viability of crowdsourcing for survey research. Behav Res Methods 43(3):800

    Google Scholar 

  39. Zhang N, Cheng N, Lu N, Zhang X, Mark JW, Shen XS (2015) Partner selection and incentive mechanism for physical layer security. IEEE Trans Wirel Commun 14(8):4265–4276

    Google Scholar 

  40. Xu G, Li H, Liu S, Yang K, Lin X (2019) VerifyNet: secure and verifiable federated learning. IEEE Transactions on Information Forensics and Security

  41. Zheng Y, Lu R, Li B, Shao J, Yang H, Choo KKR (2019) Efficient privacy-preserving data merging and skyline computation over multi-source encrypted data. Inf Sci 498:91–105

    Google Scholar 

  42. Nabil M, Sherif A, Mahmoud M, Alsharif A, Abdallah M (2019) Efficient and privacy-preserving ridesharing Organization for Transferable and non-transferable services. IEEE Transactions on Dependable and Secure Computing

  43. Wang T, Cao Z, Wang S, Wang J, Qi l.; Liu A.; Xie M.; Li X. (2019) Privacy-enhanced data collection based on deep learning for internet of vehicles. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2019.2962844

  44. Hafeez KA, Zhao L, Ma B, Mark JW (2013) Performance analysis and enhancement of the DSRC for VANET's safety application. IEEE Tran on Vehicular Technology 62(7):3069–3083

    Google Scholar 

  45. Li H, Liu D, Dai Y, Luan TH, Yu S (2018) Personalized search over encrypted data with efficient and secure updates in Mobile clouds. IEEE Trans Emerg Top Comput 6(1):97–109

    Google Scholar 

  46. Wang T, Wang P, Cai S, Ma Y, Liu A, Xie M (2019) A unified trustworthy environment based on edge computing in industrial IoT. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2019.2955152

  47. Lin J, Amini S, Hong JI, Sadeh N, Lindqvist J, Zhang J (2012) Expectation and purpose: understanding users' mental models of mobile app privacy through crowdsourcing. In proceedings of the 2012 ACM conference on ubiquitous computing: 501-510.

  48. Agarwal Y, Hall M (2013) ProtectMyPrivacy: detecting and mitigating privacy leaks on iOS devices using crowdsourcing. In proceeding of the 11th annual international conference on Mobile systems, applications, and services ACM: 97-110.

  49. Toch E (2014) Crowdsourcing privacy preferences in context-aware applications. Pers Ubiquit Comput 18(1):129–141

    Google Scholar 

  50. To H, Ghinita G, Shahabi C (2014) A framework for protecting worker location privacy in spatial crowdsourcing. Proceedings of the VLDB Endowment 7(10):919–930

    Google Scholar 

  51. Wang Y, Cai Z, Yin G, Gao Y, Tong X, Wu G (2016) An incentive mechanism with privacy protection in mobile crowdsourcing systems. Comput Netw 102:157–171

    Google Scholar 

  52. Jiang W, Li H, Xu G, Wen M, Dong G, Lin X (2019) PTAS: privacy-preserving thin-client authentication scheme in blockchain-based PKI. Futur Gener Comput Syst 96:185–195

    Google Scholar 

  53. Liu X, Wang T, Jia W, Liu A, Chi K (2019) Quick convex hull-based rendezvous planning for delay-harsh mobile data gathering in disjoint sensor networks. IEEE Transactions on System, Man, and Cybernetics: Systems, DoI. https://doi.org/10.1109/TSMC.2019.2938790

  54. Ren J, Guo H, Xu C, Zhang Y (2017) Serving at the edge: A scalable IoT architecture based on transparent computing. IEEE Netw 31(5):96–105

    Google Scholar 

  55. Li T, Zhao M, Won K (2020) Machine learning based code dissemination by selection of reliability Mobile vehicles in 5G networks. Comput Commun 152:109–118

    Google Scholar 

  56. Xu T, Wendt JB, Potkonjak M (2014) Security of IoT systems: design challenges and opportunities. In proceedings of the 2014 IEEE/ACM international conference on computer-aided design IEEE press: 417-423.

  57. Venanzi M, Rogers A, Jennings NR (2013) Trust-based fusion of untrustworthy information in crowdsourcing applications. In proceedings of the 2013 international conference on autonomous agents and multi-agent systems: 829-836.

  58. Xu G, Li H, Ren H, Yang K, Deng RH (2019) Data security issues in deep learning: attacks, countermeasures and opportunities. IEEE Commun Mag 57(11):116–122

    Google Scholar 

  59. Peng M, Liu W, Wang T, Zeng Z (2020) Relay selection joint consecutive packet routing scheme to improve performance for wake-up radio-enabled WSNs. Wirel Commun Mob Comput 2020:1–32

    Google Scholar 

  60. Liu Y, Zeng Z, Liu X, Zhu X, Bhuiyan M (2019) A novel load balancing and low response delay framework for edge-cloud network based on SDN. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2019.2951857

  61. Ye B, Wang Y, Liu L (2015) Crowd trust: A context-aware trust model for worker selection in crowdsourcing environments. In 2015 IEEE international conference on web services : 121-128.

  62. Gan X, Li Y, Wang W, Fu L, Wang X (2017) Social crowdsourcing to friends: An incentive mechanism for multi-resource sharing. IEEE Journal on Selected Areas in Communications 35(3):795–808

    Google Scholar 

  63. Xu G, Li H, Liu S, Yang K, Lin X (2020) VerifyNet: secure and verifiable federated learning. IEEE Transactions on Information Forensics and Security 15(1):911–926

    Google Scholar 

  64. Zhu X, An J, Yang M, Xiang L, Yang Q, Gui X (2016) A fair incentive mechanism for crowdsourcing in crowd sensing. IEEE Internet Things J 3(6):1364–1372

    Google Scholar 

  65. Zhang Y, Van der Schaar M (2012) Reputation-based incentive protocols in crowdsourcing applications. In 2012 proceedings IEEE INFOCOM: 2140-2148.

  66. Zhao D, Li XY, Ma H (2016) Budget-feasible online incentive mechanisms for crowdsourcing tasks truthfully. IEEE/ACM Transactions on Networking (TON) 24(2):647–661

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61772554, No.61602398, No.61672447), Hunan Provincial National Natural Science Foundation of China (No.2017JJ3316, No.2019JJ50592), Independent Exploration and Innovation Project for Graduate Students of Central South University (No. 2019zzts589).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Yingying Ren, Zhiwen Zeng & Guoming Zhi

  2. College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Tian Wang

  3. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Shaobo Zhang

Authors
  1. Yingying Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiwen Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaobo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guoming Zhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guoming Zhi.

Additional information

This article is part of the Topical Collection: Special Issue on Security and Privacy in Machine Learning Assisted P2P Networks

Guest Editors: Hongwei Li, Rongxing Lu and Mohamed Mahmoud

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, Y., Zeng, Z., Wang, T. et al. A trust-based minimum cost and quality aware data collection scheme in P2P network. Peer-to-Peer Netw. Appl. 13, 2300–2323 (2020). https://doi.org/10.1007/s12083-020-00898-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-00898-2

Keywords

Search

Navigation