Optimization of channel packet loss in wireless sensor communication systems using model predictive control

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.352116

Keywords:

wireless sensor network, WNCS, packet loss, SINR, Bernoulli process, finite-state Markov chain, TDMA, CSMA/CA, MPC

Abstract

The object of the study is an IEEE 802.15.4 (2.4 GHz) wireless networked control system (WNCS) closing the loop over a wireless sensor network. Fading and interference increase packet loss and delay, reducing stability margins and control quality. The unresolved problem is the lack of a unified end-to-end (E2E) loss model that links PHY signal quality, multi-hop routing and medium access to closed-loop behavior and can be embedded into controller synthesis. An SINR-based channel model (path loss, lognormal shadowing, multipath fading) is mapped to BER and packet error probability; E2E loss for single-hop and multi-hop routes is obtained using Bernoulli and finite-state Markov (FSMC) processes. For verification, original packet traces are captured with an IEEE 802.15.4 sniffer/logger and stored before processing (timestamp, node identifier, sequence number, RSSI/LQI and delivery outcome) to compute PER, latency and burstiness and to parameterize the SINR-to-PER mapping and loss models. Simulations show that TDMA/TSCH achieves up to 40% lower loss than CSMA/CA, while E2E loss rises from 3% to 32% as hop count increases from 1 to 8. An MPC-based co-design jointly adapts transmit power, sampling period and retransmissions. Compared with a fixed-parameter LQR baseline, E2E PER is reduced from 4.45% to 3.66%, average delay from 0.20 s to 0.12 s, and integral absolute error by 50%. The gains are attributed to reduced contention under TDMA scheduling and predictor-driven MPC adaptation. The approach targets industrial monitoring and control with fixed sampling, slowly varying interference and static multi-hop topologies, where parameters can be identified offline and used for online MPC adaptation

Author Biographies

Ainur Ormanbekova, Almaty Technological University

Doctor of Philosophy (PhD), Assistant Professor, Dean of Faculty

Department of Engineering and Information Technology

Anar Khabay, Satbayev University

Doctor PhD, Associate Professor

Department of Electronics, Telecommunications and Space Technology

Yerkebulan Tuleshov, Satbayev University

Candidate of Technical Sciences, Associate Professor

Department of Robotics and Technical Means of Automation

Nurlan Sarsenbayev, Satbayev University

Candidate of Technical Sciences, Associate Professor

Department of Automation and Сontrol

Zhazira Julayeva, Almaty Technological University

Master of Technical Sciences

Department of Automation and Robotics

Serikbek Ibekeyev, Almaty Technological University

Master of Technical Sciences, Senior Lecturer

Department of Computer Engineering

Maral Abulkhanova, Satbayev University

Senior Lecturer

Department of Electronics, Telecommunications and Space Technology

Askhat Tlegenov, Satbayev University

Master Student of Technical Sciences

Department of Electronics, Telecommunications and Space Technologies

Magzhan Igen, Satbayev University

Master’s Student in Telecommunications

Department of Electronics, Telecommunications and Space Technologies

References

  1. Pezzutto, M., Dey, S., Garone, E., Gatsis, K., Johansson, K. H., Schenato, L. (2024). Wireless control: Retrospective and open vistas. Annual Reviews in Control, 58, 100972. https://doi.org/10.1016/j.arcontrol.2024.100972
  2. Hamdan, M. M., Mahmoud, M. M. (2022). Analysis and Challenges in Wireless Networked Control System: A Survey. International Journal of Robotics and Control Systems, 2 (3), 492–522. https://doi.org/10.31763/ijrcs.v2i3.731
  3. Huang, K., Liu, W., Li, Y., Savkin, A., Vucetic, B. (2020). Wireless Feedback Control With Variable Packet Length for Industrial IoT. IEEE Wireless Communications Letters, 9 (9), 1586–1590. https://doi.org/10.1109/lwc.2020.2998611
  4. Liu, Y., Wang, J., Gomes, L., Sun, W. (2021). Adaptive Robust Control for Networked Strict-Feedback Nonlinear Systems with State and Input Quantization. Electronics, 10 (22), 2783. https://doi.org/10.3390/electronics10222783
  5. Shi, T., Guan, Y., Zheng, Y. (2022). Model predictive control of networked control systems with disturbances and deception attacks under communication constraints. International Journal of Robust and Nonlinear Control, 35 (7), 2717–2735. https://doi.org/10.1002/rnc.6363
  6. Kuatova, M., Tuleshov, A., Baurzhan, A., Jomartov, A., Tuleshov, Y., Sayakov, O. (2025). Development and Validation of An Automated Crank Press System Integrating the Stephenson II Six-Bar Linkage and Model Predictive Control. ES Materials & Manufacturing. https://doi.org/10.30919/mm1799
  7. Lee, M.-F. R., Chiu, F.-H. S., Huang, H.-C., Ivancsits, C. (2013). Generalized Predictive Control in a Wireless Networked Control System. International Journal of Distributed Sensor Networks, 9 (12), 475730. https://doi.org/10.1155/2013/475730
  8. Florenzan Reyes, L. F., Smarra, F., Lun, Y. Z., D’Innocenzo, A. (2021). Learning Markov models of fading channels in wireless control networks: a regression trees based approach. 2021 29th Mediterranean Conference on Control and Automation (MED), 232–237. https://doi.org/10.1109/med51440.2021.9480310
  9. Kim, M.-J., Lee, H.-I., Choi, J.-H., Lim, K. J., Mo, C. (2022). Development of a Soil Organic Matter Content Prediction Model Based on Supervised Learning Using Vis-NIR/SWIR Spectroscopy. Sensors, 22 (14), 5129. https://doi.org/10.3390/s22145129
  10. Silva, C. A. G. da, Santos, E. L. dos (2023). A Compensation Model for Packet Loss Using Kalman Filter in Wireless Network Control Systems. Energies, 16 (8), 3329. https://doi.org/10.3390/en16083329
  11. Yue, X., Liu, Y. (2022). Performance Analysis of Intelligent Reflecting Surface Assisted NOMA Networks. IEEE Transactions on Wireless Communications, 21 (4), 2623–2636. https://doi.org/10.1109/twc.2021.3114221
  12. Jia, Y., Ye, C., Cui, Y. (2020). Analysis and Optimization of an Intelligent Reflecting Surface-Assisted System With Interference. IEEE Transactions on Wireless Communications, 19 (12), 8068–8082. https://doi.org/10.1109/twc.2020.3019088
  13. Orea-Flores, I. Y., Rivero-Angeles, M. E., Gonzalez-Ambriz, S.-J., Anaya, E. A., Saleem, S. (2024). Performance Analysis of Wireless Sensor Networks Using Damped Oscillation Functions for the Packet Transmission Probability. Computers, 13 (11), 285. https://doi.org/10.3390/computers13110285
  14. Zila, A., Ouchatti, A., Mouzouna, Y. (2025). Exploring Node Failure and Packet Loss in Wireless Sensor Networks: A Comprehensive Simulation Analysis. International Journal of Communication Systems, 38 (13). https://doi.org/10.1002/dac.70174
  15. Lun, Y. Z., Rinaldi, C., D’Innocenzo, A., Santucci, F. (2024). Co-Designing Wireless Networked Control Systems on IEEE 802.15.4-Based Links Under Wi-Fi Interference. IEEE Access, 12, 71157–71183. https://doi.org/10.1109/access.2024.3402082
  16. Vayssade, T., Azais, F., Latorre, L., Lefevre, F. (2019). Low-Cost Digital Test Solution for Symbol Error Detection of RF ZigBee Transmitters. IEEE Transactions on Device and Materials Reliability, 19 (1), 16–24. https://doi.org/10.1109/tdmr.2019.2898769
  17. Díez, V., Arriola, A., Val, I., Velez, M. (2020). Reliability evaluation of point-to-point links based on IEEE 802.15.4 physical layer for IWSAN applications. AEU - International Journal of Electronics and Communications, 113, 152967. https://doi.org/10.1016/j.aeue.2019.152967
  18. Barac, F., Gidlund, M., Zhang, T. (2014). Scrutinizing Bit- and Symbol-Errors of IEEE 802.15.4 Communication in Industrial Environments. IEEE Transactions on Instrumentation and Measurement, 63 (7), 1783–1794. https://doi.org/10.1109/tim.2013.2293235
  19. Chen, D., Zhuang, Y., Huai, J., Sun, X., Yang, X., Awais Javed, M. et al. (2021). Coexistence and Interference Mitigation for WPANs and WLANs From Traditional Approaches to Deep Learning: A Review. IEEE Sensors Journal, 21 (22), 25561–25589. https://doi.org/10.1109/jsen.2021.3117399
  20. Yang, D., Xu, Y., Gidlund, M. (2011). Wireless Coexistence between IEEE 802.11- and IEEE 802.15.4-Based Networks: A Survey. International Journal of Distributed Sensor Networks, 7 (1), 912152. https://doi.org/10.1155/2011/912152
  21. Yu, Y.-S., Chen, Y.-S. (2020). A Measurement-Based Frame-Level Error Model for Evaluation of Industrial Wireless Sensor Networks. Sensors, 20 (14), 3978. https://doi.org/10.3390/s20143978
  22. Brown, J., Roedig, U., Boano, C. A., Romer, K. (2014). Estimating packet reception rate in noisy environments. 39th Annual IEEE Conference on Local Computer Networks Workshops, 583–591. https://doi.org/10.1109/lcnw.2014.6927706
  23. Satoh, D., Kobayashi, K., Yamashita, Y. (2018). MPC-based Co-design of Control and Routing for Wireless Sensor and Actuator Networks. International Journal of Control, Automation and Systems, 16 (3), 953–960. https://doi.org/10.1007/s12555-017-0170-7
  24. Kidane, Z. M., Dargie, W. (2025). Impact of Cross Technology Interference on Time Synchronization and Join Time in Low-Power Wireless Networks. https://doi.org/10.2139/ssrn.5119414
  25. Balbi, M., Doherty, L., Watteyne, T. (2025). A comprehensive survey on channel hopping and scheduling enhancements for TSCH networks. Journal of Network and Computer Applications, 238, 104164. https://doi.org/10.1016/j.jnca.2025.104164
  26. Kidane, Z. M., Dargie, W. (2025). Cross-technology interference: detection, avoidance, and coexistence mechanisms in the ISM bands. CCF Transactions on Pervasive Computing and Interaction, 7 (3), 356–375. https://doi.org/10.1007/s42486-025-00185-0
  27. Wen, H., Lin, C., Chen, Z.-J., Yin, H., He, T., Dutkiewicz, E. (2009). An Improved Markov Model for IEEE 802.15.4 Slotted CSMA/CA Mechanism. Journal of Computer Science and Technology, 24 (3), 495–504. https://doi.org/10.1007/s11390-009-9240-5
  28. Kim, S., Kim, B.-S., Kim, K. H., Kim, K.-I. (2019). Opportunistic Multipath Routing in Long-Hop Wireless Sensor Networks. Sensors, 19 (19), 4072. https://doi.org/10.3390/s19194072
  29. Marelli, D. E., Sui, T., Rohr, E. R., Fu, M. (2019). Stability of Kalman filtering with a random measurement equation: Application to sensor scheduling with intermittent observations. Automatica, 99, 390–402. https://doi.org/10.1016/j.automatica.2018.11.003
  30. Li, P., Zhao, Y.-B., Kang, Y. (2022). Integrated Channel-Aware Scheduling and Packet-Based Predictive Control for Wireless Cloud Control Systems. IEEE Transactions on Cybernetics, 52 (5), 2735–2749. https://doi.org/10.1109/tcyb.2020.3019179
  31. Liu, W., Quevedo, D. E., Li, Y., Johansson, K. H., Vucetic, B. (2022). Remote State Estimation With Smart Sensors Over Markov Fading Channels. IEEE Transactions on Automatic Control, 67 (6), 2743–2757. https://doi.org/10.1109/tac.2021.3090741
  32. Girgis, A. M., Park, J., Bennis, M., Debbah, M. (2021). Predictive Control and Communication Co-Design via Two-Way Gaussian Process Regression and AoI-Aware Scheduling. IEEE Transactions on Communications, 69 (10), 7077–7093. https://doi.org/10.1109/tcomm.2021.3099156
Optimization of channel packet loss in wireless sensor communication systems using model predictive control

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Published

2026-02-27

How to Cite

Ormanbekova, A., Khabay, A., Tuleshov, Y., Sarsenbayev, N., Julayeva, Z., Ibekeyev, S. ., Abulkhanova, M., Tlegenov, A., & Igen, M. (2026). Optimization of channel packet loss in wireless sensor communication systems using model predictive control. Eastern-European Journal of Enterprise Technologies, 1(9 (139), 56–67. https://doi.org/10.15587/1729-4061.2026.352116

Issue

Vol. 1 No. 9 (139) (2026): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2026 Ainur Ormanbekova, Anar Khabay, Yerkebulan Tuleshov, Nurlan Sarsenbayev, Zhazira Julayeva, Serikbek Ibekeyev, Maral Abulkhanova, Askhat Tlegenov, Magzhan Igen

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