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Research on reliability of Internet of Things RFID based on improved random hash protocol and cooperative game in low-carbon supply chain environment

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Abstract

With the maturity of RFID technology and the rapid development of mobile intelligent terminals, the Internet of Things (IOT) has attracted more and more attention and will become another revolution after the Internet. However, compared with traditional Internet applications, Internet of Things applications supported by RFID devices and intelligent terminals have more complex and serious security problems. For example, trust mechanism and malicious behavior detection have become the key issues to be solved in the construction of secure and trusted Internet of Things. The in-depth analysis and research of trust mechanism is of great significance to improve the security of the infrastructure of the Internet of Things and even the whole security system of the Internet of Things. Aiming at the relevant requirements of Internet of Things credibility in low-carbon supply chain environment, this paper proposes an improved random hash protocol and assistant game-based RFID credibility of Internet of Things. Combining with random oracle model security theory (ROM), experiments are carried out with data. A collaborative game method is proposed. Through collaboration with intra-agency nodes, inconsistent nodes and their observations are analyzed. Experiments show that simple game and cooperative game can effectively suppress malicious attacks in normal networks and malicious node–dominated networks, respectively. The protocol achieves the advantages of access authentication, anonymity security, anti-retransmit, anti-traceability, data accountability, time-scaling, and cost-effectiveness, and can effectively enhance the credibility of the Internet of Things in a low-carbon supply chain environment.

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References

  1. Rajaraman V (2017) Radio frequency identification. Resonance 22(6):549–575

    Article  Google Scholar 

  2. Pescetto P, Pellegrino G (2018) Automatic tuning for sensorless commissioning of synchronous reluctance machines augmented with high frequency voltage injection. IEEE Trans Ind Appl 11(9):168–183

    Google Scholar 

  3. Purushothaman G, Prakash PR, Kothari S (2018) Investigation of multiple frequency recognition from single-channel steady-state visual evoked potential for efficient brain–computer interfaces application. Iet Sign Process 12(3):255–259

    Article  Google Scholar 

  4. Hase A, Mishina H (2018) Identification and evaluation of wear phenomena under electric current by using an acoustic emission technique. Tribol Int 64:145–161

    Google Scholar 

  5. Hartcher KM, Hickey KA, Hemsworth PH, Cronin GM, Wilkinson SJ, Singh M (2016) Relationships between range access as monitored by radio frequency identification technology, fearfulness, and plumage damage in free-range laying hens. Animal 10(5):847–853

    Article  Google Scholar 

  6. Bolaños F, Ledue JM, Murphy TH (2017) Cost effective raspberry pi-based radio frequency identification tagging of mice suitable for automated in vivo imaging. J Neurosci Methods 276:79–83

    Article  Google Scholar 

  7. Dufour JC, Reynier P, Boudjema S, Soto Aladro A, Giorgi R, Brouqui P (2017) Evaluation of hand hygiene compliance and associated factors with a radio-frequency-identification-based real-time continuous automated monitoring system. J Hosp Infect 95(4):344–357

    Article  Google Scholar 

  8. Barge P, Gay P, Merlino V et al (2017) Radio frequency identification technologies for livestock management. Can J Anim Sci 93(1):23–33

    Article  Google Scholar 

  9. Bachtobji S, Omri A, Bouallegue R, Raoof K (2018) Modelling and performance analysis of mmWaves and radio-frequency based 3D heterogeneous networks. IET Commun 12(3):290–296

    Article  Google Scholar 

  10. Awan SA, Pan G, Taan LMA et al (2018) Radio-frequency transport electromagnetic properties of chemical vapour deposition graphene from direct current to 110 MHz. IET Circ Devices Syst 9(1):46–51

    Article  Google Scholar 

  11. Li P, Lang Z, Zhao L et al (2018) System identification-based frequency domain feature extraction for defect detection and characterization. NDT&E Int 98:70–79

    Article  Google Scholar 

  12. Kgwadi M, Rizwan M, Kutty AA et al (2016) Performance comparison of inkjet and thermal transfer printed passive ultra-high-frequency radio-frequency identification tags. IET Microwaves Antennas Propag 10(14):1507–1514

    Article  Google Scholar 

  13. Mousavi N, Sharifkhani M, Jalali M (2016) Ultra-low power current mode all- MOS ASK demodulator for radio frequency identification applications. IET Circ Devices Syst 10(2):130–134

    Article  Google Scholar 

  14. Rockelé M, Vasseur K, Mityashin A et al (2018) Integrated tin monoxide P-channel thin-film transistors for digital circuit applications. IEEE Trans Electron Devices 65(2):514–519

    Article  Google Scholar 

  15. Hogan MT, Edge AC, Geach JE et al (2018) High radio-frequency properties and variability of brightest cluster galaxies. Mon Not R Astron Soc 453(2):1223–1240

    Article  Google Scholar 

  16. Combs AW, Shiroma WA, Ohta AT (2018) Ferrofluidic actuation of liquid metal for radio-frequency applications. Electron Lett 54(3):151–153

    Article  Google Scholar 

  17. Fu S, Xu Z, Lu J et al (2018) Modulation format identification enabled by the digital frequency-offset loading technique for hitless coherent transceiver. Opt Express 26(6):755–769

    Article  Google Scholar 

  18. Qian L, Zheng L, Shang Y, Zhang Y, Zhang Y (2018) Alzheimer’s disease Neuroimaging Initiative. Intrinsic frequency specific brain networks for identification of MCI individuals using resting-state fMRI. Neurosci Lett 664:7–14

    Article  Google Scholar 

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Correspondence to Lijian Yu.

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Yu, L., Shiue, YC. Research on reliability of Internet of Things RFID based on improved random hash protocol and cooperative game in low-carbon supply chain environment. Pers Ubiquit Comput 23, 583–594 (2019). https://doi.org/10.1007/s00779-019-01215-2

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  • DOI: https://doi.org/10.1007/s00779-019-01215-2

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