Abstract
To enhance the anti-attack capability of inter-domain wireless sensor networks and ensure secure information transmission, a secure transmission algorithm for multiplexing inter- domain wireless sensor network information is proposed. This algorithm utilizes the inter- domain structure of wireless sensor networks to analyze channel transmission characteristics, integrating the network information transmission principles of multiplexing technology to construct a robust communication framework. The transmission model of inter-domain multiplexed information in wireless sensor networks is constructed. This model primarily relies on the HRC-MAC protocol, leveraging the characteristics of code division multiplexing to perform uplink spread spectrum and downlink modulation of information. It also incorporates the threshold method for information encryption and transmission control, which can improve the security of information transmission while reducing the fading loss of channel transmission. The experiment constructs a wireless sensor network environment and uses the algorithm for secure information transmission. The experimental results show that the channel transmission security capacity of the algorithm exceeds 1.5 bps/Hz, and the transmission security rate meets the standard. The transmission security factors of the channel within the domain exceed 0.917. The anti-attack performance of information encryption exceeds 90.2%. It can effectively disrupt the information sequence and significantly prevent tampering during information transmission.
References
Rani, K. P., Sreedevi, P., Poornima, E., and Sri, T. S. (2023). FTOR-Mod PSO: A fault tolerance and an optimal relay node selection algorithm for wireless sensor networks using modified PSO. Knowledge-Based Systems, 272(July 19): 110583.1–110583.12.
Chenthil, T. R., and Jayarin, P. J. (2022). An energy aware multi slot scheduling with two-layer hexagonal based integrated aggregation approach for underwater wireless sensor networks (UWSN). Journal of Interconnection Networks, 22(4): 44–71.
Pang, F., and Dou L. J. (2023). Research on end-to-end secure rerouting protocol between multi domains in WLAN. Computer Simulation,40(06): 444–448.
Rajasoundaran, S., Prabu, A. V., Routray, S., Malla, P. P., Kumar, G. S., and Mukherjee, A., et al. (2022). Secure routing with multi-watchdog construction using deep particle convolutional model for IoT based 5G wireless sensor networks. Computer Communications, 187(Apr.): 71–82.
Anselme, R. A. M., and Satori, H. (2024). Machine learning attack detection based-on stochastic classifier methods for enhancing of routing security in wireless sensor networks. Ad Hoc Networks, 163(c):103581.1-103581.9.
Nguyen, T. T., Dao, T. K., Nguyen, T. D., and Nguyen, V. T. (2023). An improved honey badger algorithm for coverage optimization in wireless sensor network. Journal of Internet Technology, 24(2): 363–377.
Sureshkumar, K., and Vimala, P. (2023). Simultaneous wireless information and power transmission-based power transfer and energy prediction for efficient communication with golden Taylor sea lion optimization in wireless sensor network. International Journal of Communication Systems, 36(13): e5544.1–e5544.28.
Balamurugan, V., Karthikeyan, R., Sundaravadivazhagan, B., and Cyriac, R. (2023). Enhanced Elman spike neural network based fractional order discrete Tchebyshev encryption fostered big data analytical method for enhancing cloud data security. Wireless Networks, 29(2): 523–537.
Vijayakumar, M. (2022). Network statistics-based routing and path orient data encryption scheme for efficient healthcare monitoring with IoT in WSN. International Journal of Communication Systems, 36(1): e5361.1–e5361.12.
Bethi, S., and Moparthi, N. R. (2022). Adaptive secure energy efficiency routing protocol for wireless sensor network. Journal of Mobile Multimedia, 18(4): 1009–1034.
Wang, X., Liang, X., Yang, C., Yuan, Y., Liu, G., Wang, J., and Wang, J. (2024). Performance analysis of pre-distorted layered asymmetrically clipped optical orthogonal frequency division multiplexing. In 2024 IEEE 30th International Conference on Telecommunications (ICT), 106(6): 24–27.
Lang, O., Hofbauer, C., Feger, R., and Huemer, M. (2023). Range-division multiplexing for MIMO OFDM joint radar and communications. IEEE Transactions on Vehicular Technology, 72(1): 52–65.
Bhat, S. J., and Santhosh, K. V. (2022). An artificial hummingbird algorithm-based localization with reduced number of reference nodes for wireless sensor networks. Physical Communication, 55(Dec.): 101921.1–101921.8.
Mozaffari, M., Mazinani, S. M., and Khazaei, A. A. (2025). An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks. Wireless Networks, 31(1): 109–125.
Ghosh, T., Roy, A., Misra, S., and Raghuwanshi, N. S. (2022). CASE: A context- aware security scheme for preserving data privacy in IoT-enabled society 5.0. IEEE Internet of Things Journal, 9(4): 2497–2504.
Ayyadurai, M., Seetha, J., Haque, S. M. F. U., Juliana, R., and Karthikeyan, C. (2023). Routing algorithm for underwater acoustic sensor network. Neural Processing Letters, 55(1): 441–457.
Raut, G., Biasizzo, A., Dhakad, N., Gupta, N., Papa, G., and Vishvakarma, S. K. (2022). Data multiplexed and hardware reused architecture for deep neural network accelerator. Neurocomputing, 486(May 14): 147–159.
Chakraborty, D., Mukherjee, P., Sarkar, S., and Das, N. R. (2025). Intelligent detection threshold optimization in WDM systems: A machine learning approach for crosstalk mitigation. Optical and Quantum Electronics, 57(11): 618–624.
Kotb, M. M. E., Abdel-Haleem, M. R., Hassan, A. Y., Plawiak, P., and Mohra, A. S. (2025). Hybrid time synchronization and ANN-based M-QAM demodulation for enhanced performance in OFDM and MIMO-OFDM systems. Neural Computing & Applications, 37(31): 26249–26279.
Priyadharsini, N. A., Kumar, J. A., and Baby, S. R. (2024). A hybrid multicarrier modulation and multiplexing scheme for beyond 5G systems. Wireless Personal Communications: An International Journal, 138(1): 369–385.
Pan, P., Su, Y., Pan, G., et al. (2024). A secure transmission scheme with efficient and lightweight group key generation for underwater acoustic sensor networks. IEEE Internet of Things Journal, 11(23): 37916–37927.
Zarei, M., Dindarlou, M. H. F., Taghizadeh, M., et al. (2025). Enhancing the LEACH protocol and lightweight chaotic cryptography for secure data transmission in wireless sensor networks. Scientific Reports, 15(1): 42323–42328.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2026 Journal of Cyber Security and Mobility
