V.I. Parfenov1, Bui Trong Tien2, Cong Minh Tuan3, A.V. Terekhov4
1,2 Voronezh State University (Voronezh, Russia)
2 National Research University “MPEI” (Moscow, Russia)
1 vip@phys.vsu.ru, 2 trongtienpt98@gmail.com, 3 kongm@mpei.ru, 4 aleksandr-terekhov@ya.ru
The amount of transmitted information in wireless sensor networks usually needs to be limited due to the small size of sensors, limited energy capacity of their power supplies and other reasons. All this requires information processing and decision making directly in the sensors themselves. At the same time, the transmission of this information to the central node that makes the final decision can be carried out in different ways. It is usually believed that it is advisable to construct such a network using a star topology. However, this is not always possible. In this connection, in the paper two new approaches are proposed to the transmission and processing of information in sensors. For these two approaches, two optimal algorithms for information processing are synthesized, namely, the algorithms for complex detection of the observed object, and their efficiency is calculated. A comparison of the probabilities of the total error for these algorithms is performed depending on the number of sensors and the signal-to-noise ratio. The influence of the coordinates of the observed object on the efficiency of its detection is studied. The obtained results allow making a reasonable choice of the parameters of the wireless sensor network to solve the specified problem under the given conditions.
Parfenov V.I., Bui Trong Tien, Cong Minh Tuan, Terekhov A.V. Algorithms for interconnecting in the serial transmission of information in wireless sensor networks when solving the problem of detecting the objects of observation. Radiotekhnika. 2025. V. 89. № 6.
P. 40−52. DOI: https://doi.org/10.18127/j00338486-202506-04 (In Russian)
- Nurlan Z., Zhukabayeva T., Othman M., Adamova A., Zhakiyev N. Wireless sensor network as a mesh: Vision and сhallenges. IEEE Access. 2021. V. 10. P. 46-67. https://doi.org/10.1109/ACCESS.2021.3137341.
- Manuel E.M., Pankajakshan V., Mohan M.T. Efficient strategies for signal aggregation in low-power wireless sensor networks with discrete transmission ranges. IEEE Sensors Letters. 2023. V. 7(3). P. 1-4. https://doi.org/10.1109/LSENS.2023.3250432.
- Choi H.H., Muy S., Lee J.R. Geometric analysis-based cluster head selection for sectorized wireless powered sensor networks. IEEE Wireless Communications Letters. 2021. V. 10(3). P. 649-653. https://doi.org/10.1109/LWC.2020.3044902.
- Qiu Z., Ma Y., Fan F., Huang J., Wu M. Adaptive scale patch-based contrast measure for dim and small infrared target detection. IEEE Geoscience and Remote Sensing Letters. 2022. V. 19. P. 1-5. https://doi.org/10.1109/LGRS.2020.3036842.
- Rao M., Kamila N.K. Target tracking in wireless sensor networks: The current state of art. Sensor technology: Concepts, methodologies, tools, and applications. Hershley: IGI Global. 2020. V. 2. P. 857-880. https://doi.org/10.4018/978-1-7998-2454-1.ch041
- Amutha J., Sharma S., Nagar J. WSN strategies based on sensors, deployment, sensing models, coverage and energy efficiency: Re-view, approaches and open issues. Wireless Personal Communications. 2020. V. 111. P. 1089-1115. https://doi.org/10.1007/s11277-019-06903-z.
- Surenther I., Sridhar K.P., Roberts M.K. Maximizing energy efficiency in wireless sensor networks for data transmission: A deep learn-ing-based grouping model approach. Alexandria Engineering Journal. 2023. V. 83. P. 53-65. https://doi.org/10.1016/j.aej.2023.10.016.
- Hammad M., Bsoul M., Hammad M., Al-Hawawreh M. An efficient approach for representing and sending data in wireless sensor networks. Journal of Communications. 2019. V. 14(2). P. 104-109. https://doi.org/10.12720/jcm.14.2.104-109.
- Zhang H. Analysis and research on wireless sensor networks. 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020). Sanya. China. 2020. Р. 1-7. https://doi.org/10.1051/matecconf/202133604009.
- Dao T.K., Chu S.C., Nguyen T.T., Nguyen T.D., Nguyen V.T. An optimal WSN node coverage based on enhanced Archimedes optimization algorithm. Entropy. 2022. V. 24(8). P. 1-22. https://doi.org/10.3390/e24081018.
- Lewandowski M., Płaczek B. Data transmission reduction in wireless sensor network for spatial event detection. Sensors. 2021.
V. 21(21). P. 1-21. https://doi.org/10.3390/s21217256. - Zhang J., Lin Z., Tsai P.W., Xu L. Entropy-driven data aggregation method for energy-efficient wireless sensor networks. Information Fusion. 2020. V. 56. P. 103–113. https://doi.org/10.1016/j.inffus.2019.10.008.
- Varshney P.K. Distributed detection and data fusion. New York: Springer. 1997. 276 p.
- Induja K., Krupa A.J. A connectivity protocol for star topology using wireless sensor network. 2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2). Chennai. India. 2017. Р. 50-56. https://doi.org/10.1109/ICNETS2.2017.8067896.
- Goratti L., Baykas T., Rasheed T., Kato S. NACRP: A connectivity protocol for star topology wireless sensor networks. IEEE Wireless Communications Letters. 2016. V. 5(2). P. 120-123. https://doi.org/10.1109/LWC.2015.2506163.
- Parfenov V.I., Le V.D. Optimal fusion rule for distributed detection with channel errors taking into account sensors’ unreliability probability when protecting coastlines. International Journal of Sensor Networks. 2022. V. 38(2). P. 71-84. https://doi.org/10.1504/IJSNET.2022.121157.
- Skljar B. Cifrovaja svjaz'. Teoreticheskie osnovy i prakticheskoe primenenie. M.: ID «Vil'jams». 2003. 1104 s. (in Russian).

