Ensuring Security in Multi-Tag Backscatter Communication Systems

Authors

  • Zilin You China Southern Power Grid (China) image/svg+xml , Guangdong Provincial Key Laboratory of Power System Network Security
  • Zhihong Liang China Southern Power Grid (China) image/svg+xml , Guangdong Provincial Key Laboratory of Power System Network Security
  • Siliang Suo China Southern Power Grid (China) image/svg+xml , Guangdong Provincial Key Laboratory of Power System Network Security
  • Lifeng Mai China Southern Power Grid (China) image/svg+xml , Guangdong Provincial Key Laboratory of Power System Network Security
  • Qiaoling Lin China Southern Power Grid (China) image/svg+xml , Guangdong Provincial Key Laboratory of Power System Network Security

DOI:

https://doi.org/10.4108/eetsis.9691

Keywords:

Power data network, multi-tag, secure transmission, backscatter communication

Abstract

This paper studies the security performance of a multi-tag backscatter communication system, where a reader exchanges its information with multiple distributed tags in the presence of an eavesdropper. The system security performance is assessed using the secrecy outage probability (SOP), which quantifies the likelihood that the system secrecy capacity drops below a specified threshold. The paper provides a mathematical analysis on the communication links, considering Rayleigh fading, and investigates the effect of system monostatic and bistatic RFID configurations on the overall security. An optimal tag selection scheme is proposed to maximize the secrecy capacity by choosing the tag with the best link between the reader and the eavesdropper. Moreover, an analytical expression is derived for the system SOP, which incorporates the joint probability distributions of channel gains and the asymptotic SOP is provided as the signal-to-noise ratio increases. Simulation results are presented to verify the theoretical analysis, where the simulation results closely match the analytical predictions, confirming the accuracy of the proposed SOP analysis. Moreover, the asymptotic analysis offers an upper bound on the outage probability, consistent with theoretical analysis.

References

1] S. S. Saab, D. Shen, M. Orabi, D. Kors, and R. H. Jaafar, “Iterative learning control: Practical implementation and automation,” IEEE Trans. Ind. Electron., vol. 69, no. 2, pp. 1858–1866, 2022.

[2] T. Klopot, P. Skupin, P. Grelewicz, and J. Czeczot, “Practical plc-based implementation of adaptive dynamic matrix controller for energy-efficient control of heat sources,” IEEE Trans. Ind. Electron., vol. 68, no. 5, pp. 4269–4278, 2021.

[3] K. Yang, D. Liu, S. Baldi, and C. Lv, “On practical implementations of connected vehicles: The issue Ensuring Security in Multi-Tag Backscatter Communication Systems acceleration feedback,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 12, pp. 21 035–21 046, 2024.

[4] Y. Wu, D. Xu, D. W. K. Ng, R. Schober, and W. H. Gerstacker, “Globally optimal resource allocation design for discrete phase shift irs-assisted multiuser networks with perfect and imperfect CSI,” IEEE Trans. Wirel. Commun., vol. 24, no. 2, pp. 1306–1324, 2025.

[5] S. Kurma, K. Singh, P. K. Sharma, C. Li, and T. A. Tsiftsis, “On the performance analysis of full-duplex cell-free massive MIMO with user mobility and imperfect CSI,” IEEE Trans. Commun., vol. 73, no. 5, pp. 3683–3701, 2025.

[6] M. A. Ajay, K. Agrawal, S. K. Singh, K. Singh, S. Prakriya, and C. Li, “Performance of battery-assisted EH fullduplex NOMA network with FBL driven mode switching under imperfect CSI and SIC,” IEEE Trans. Commun., vol. 73, no. 6, pp. 4486–4502, 2025.

[7] C. Li, Y. Yang, J. Song, X. Zhang, and Q. Xu, “A short term power load visualization forecasting method based on 2DVMD and ConvLSTM,” Southern Power System Technology, vol. 19, no. 2, pp. 1–9, 2025.

[8] C. Ye, J. Chen, X. Ma, and Z. Hu, “Anti-UAV target detection algorithm for substation based on improved YOLOv5,” Southern Power System Technology, vol. 18, no. 2, pp. 89–97, 2024.

[9] X. Liu, H. Zhang, K. Long, A. Nallanathan, and V. C. M. Leung, “Distributed unsupervised learning for interference management in integrated sensing and communication systems,” IEEE Trans. Wirel. Commun., vol. 22, no. 12, pp. 9301–9312, 2023.

[10] A. Rahmati, S. Hosseinalipour, Y. Yapici, X. He, I. Güvenç, H. Dai, and A. Bhuyan, “Dynamic interference management for uav-assisted wireless networks,” IEEE Trans. Wirel. Commun., vol. 21, no. 4, pp. 2637–2653, 2022.

[11] J. Huang, C. Yang, S. Zhang, F. Yang, O. Alfarraj, V. Frascolla, S. Mumtaz, and K. Yu, “Reinforcement learning based resource management for 6g-enabled miot with hypergraph interference model,” IEEE Trans. Commun., vol. 72, no. 7, pp. 4179–4192, 2024.

[12] X. Zhang, J. Li, J. Wu, G. Chen, Y. Meng, H. Zhu, and X. Zhang, “Binary-level formal verification based automatic security ensurement for PLC in industrial iot,” IEEE Trans. Dependable Secur. Comput., vol. 22, no. 3, pp. 2211–2226, 2025.

[13] F. Li, J. Wang, W. Xie, N. Tong, and D. Wang, “X-RAFT: improve RAFT consensus to make blockchain better secure edgeai-human-iot data,” IEEE Trans. Emerg. Top. Comput., vol. 13, no. 1, pp. 22–33, 2025.

[14] M. Liang, K. Liu, R. Gao, and Y. Li, “Integrating gpu-accelerated for fast large-scale vessel trajectories visualization in maritime iot systems,” IEEE Trans. Intell. Transp. Syst., vol. 26, no. 3, pp. 4048–4065, 2025.

[15] M. Liu, H. Peng, P. Zhang, M. Zeng, Z. Zhu, A. A. Boulogeorgos, K. Dev, and X. Li, “Ris-segmented symbiotic covert cooperative backscatter communication systems,” IEEE Trans. Veh. Technol., vol. 74, no. 1, pp. 1708–1712, 2025.

[16] J. Chen, Q. Guan, Y. Rong, D. Li, W. Chen, and H. Yu, “High order time shift keying modulation for ambient backscatter communications,” IEEE Trans. Commun., vol. 73, no. 6, pp. 3792–3803, 2025.

[17] Q. Zhang, Z. Wang, C. Zhou, S. Wang, D. Zhou, S. Jiang, L. Cai, and Y. Li, “A packaging scheme enabling resonant frequency tuning and magnetic shielding for electromagnetic vibration energy harvesters toward industrial applications,” IEEE Trans. Instrum. Meas., vol. 74, pp. 1–10, 2025.

[18] K. Huang, W. Cao, Y. Liu, D. Wu, C. Yang, and W. Gui, “A weighted deep learning-based predictive control for multimode nonlinear system with industrial applications,” IEEE Trans Autom. Sci. Eng., vol. 22, pp. 10 814–10 826, 2025.

[19] K. Huang, X. Ying, D. Wu, C. Yang, and W. Gui, “A generalized integrated fuzzy-mpc with optimal input excitation for complex systems with industrial applications,” IEEE Trans. Fuzzy Syst., vol. 33, no. 5, pp. 1415–1428, 2025.

[20] Y. Zhang, Y. Ko, R. F. Woods, and A. Marshall, “Defining spatial secrecy outage probability for exposure region based beamforming,” IEEE Trans. Wirel. Commun., vol. 16, no. 2, pp. 900–912, 2017.

[21] J. D. V. Sánchez, L. F. Urquiza-Aguiar, H. R. C. Mora, N. V. O. Garzón, and D. P. M. Osorio, “Fluid antenna system: Secrecy outage probability analysis,” IEEE Trans. Veh. Technol., vol. 73, no. 8, pp. 11 458–11 469, 2024.

[22] B. Li, Y. Zou, J. Zhou, F. Wang, W. Cao, and Y. Yao, “Secrecy outage probability analysis of friendly jammer selection aided multiuser scheduling for wireless networks,” IEEE Trans. Commun., vol. 67, no. 5, pp. 3482–3495, 2019.

[23] R. K. Mallik, “Multiplexing with multi-level ASK and noncoherent MIMO in rayleigh fading,” IEEE Trans. Commun., vol. 72, no. 10, pp. 6162–6177, 2024.

[24] J. Gao, Y. Wu, G. Caire, W. Yang, H. V. Poor, and W. Zhang, “Unsourced random access in MIMO quasistatic ray leigh fading channels: Finite block length and scaling law analyses,” IEEE Trans. Inf. Theory, vol. 71, no. 6, pp. 4342–4373, 2025.

[25] R. K. Mallik and R. D. Murch, “Rayleigh fading channel capacity for coherent signaling with asymmetric constellations,” IEEE Trans. Commun., vol. 70, no. 4, pp. 2342–2357, 2022.

[26] X. He, R. Zhou, Q. Fan, X. Xiao, Y. Yu, and Z. Yan, “Preparing student teachers for professional development: Mentoring generative artificial intelligence (AI) learners in mathematical problem solving,” IEEE Trans. Learn. Technol., vol. 18, pp. 458–469, 2025.

[27] A. Cuenca, H. Moncayo, and G. Gavilanez, “Artificial intelligence- assisted geomagnetic navigation framework,” IEEE Trans. Aerosp. Electron. Syst., vol. 61, no. 2, pp. 2477–2490, 2025.

[28] J. C. Ku and S. L. Chen, “The deployment and implementation of cloud platform for remote automatic correction of artificial intelligence models,” IEEE Trans. Ind. Informatics, vol. 21, no. 4, pp. 3466–3474, 2025.

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Published

12-01-2026

Issue

Section

AIGC - Empowered Covert Communications for Scalable Information Systems

How to Cite

1.
You Z, Liang Z, Suo S, Mai L, Lin Q. Ensuring Security in Multi-Tag Backscatter Communication Systems. EAI Endorsed Scal Inf Syst [Internet]. 2026 Jan. 12 [cited 2026 Jan. 12];12(6). Available from: https://publications.eai.eu/index.php/sis/article/view/9691