AISR: A Protocol-Closed Architecture for Interference-Aware Channel-Mask Control in Industrial LoRaWAN
Downloads
Abstract
Industrial Long Range Wide Area Network (LoRaWAN) telemetry operates under variable propagation, contention, external interference, regional spectrum rules, and limited downlink capacity. An adaptive channel policy is useful only when the network can observe alternatives, predict a benefit over the time required to deliver control, issue a legal command that the device accepts, and verify that the realized transmissions improve application outcomes after sensing and reconfiguration cost. This paper reconstructs Adaptive Interference-Aware Spectrum Reallocation (AISR) as a protocol-closed network-side controller. A gateway or dedicated monitor supplies calibrated channel observations; a causal predictor estimates packet success, energy, and deadline risk; a constrained optimizer selects device-specific legal channel masks; and a verification layer records command acceptance, actual channel use, delivery, latency, and energy. The proposed architecture introduces control age, abstention, fallback, hysteresis, and auditability as first-class design variables. It also separates the evidential roles of the available harbor RSSI/SNR localization dataset from those of a genuine intervention trial. The harbor record can characterize observed link variation, but it lacks simultaneous counterfactual channel measurements, a complete attempted-packet denominator, MAC-command traces, application delivery times, and device-current measurements. No performance improvement is therefore claimed. A staged evaluation protocol compares fixed plans, configured Adaptive Data Rate (ADR), a valid history-based policy, published allocation families, and AISR under identical traffic, sensing budgets, channel conditions, and control authority. The resulting framework makes interference-aware LoRaWAN channel management falsifiable at the level of radio observation, protocol execution, and application benefit.
Keywords
Industrial long range wide area network Channel-mask management Interference prediction Adaptive data rate Packet delivery Control age Device energy ReproducibilityReferences (36)
- [1] Sundaram, J. P. S., Du, W., & Zhao, Z. (2019). A survey on LoRa networking: Research problems, current solutions, and open issues. IEEE Communications Surveys & Tutorials, 22(1), 371–388. https://doi.org/10.1109/COMST.2019.2949598
- [2] Jouhari, M., Saeed, N., Alouini, M.S., & Amhoud, E. M. (2023). A survey on scalable LoRaWAN for massive IoT: Recent advances, potentials, and challenges. IEEE Communications Surveys & Tutorials, 25(3), 1841–1876. https://doi.org/10.1109/COMST.2023.3274934
- [3] Haxhibeqiri, J., Poorter, E. De, Moerman, I., & Hoebeke, J. (2018). A survey of LoRaWAN for IoT: From technology to application. Sensors, 18(11), 3995. https://doi.org/10.3390/s18113995
- [4] Sun, Z., Yang, H., Liu, K., Yin, Z., Li, Z., & Xu, W. (2022). Recent advances in LoRa: A comprehensive survey. ACM Transactions on Sensor Networks, 18(4), 1–44. https://doi.org/10.1145/3543856
- [5] Ikpehai, A., Adebisi, B., Rabie, K. M., Anoh, K., Ande, R. E., Hammoudeh, M., ..., & Mbanaso, U. M. (2018). Low-power wide area network technologies for internet-of-things: A comparative review. IEEE Internet of Things Journal, 6(2), 2225-2240. https://doi.org/10.1109/JIOT.2018.2883728
- [6] Povalac, A., Kral, J., Arthaber, H., Kolar, O., & Novak, M. (2023). Exploring lorawan traffic: In-depth analysis of iot network communications. Sensors, 23(17), 7333. https://doi.org/10.3390/s23177333
- [7] Alliance, L. (2022). RP002-1.0. 4 Regional Parameters. https://resources.lora-alliance.org/technical-specifications/rp002-1-0-4-regional-parameters
- [8] Fawaz, H., Khawam, K., Lahoud, S., Adjih, C., & Martin, S. (2020). Joint spreading factor and channel assignment in multi-operator LoRaWAN deployments. Sensors, 21(1), 162. https://doi.org/10.3390/s21010162
- [9] Moraes, J., Oliveira, H., Cerqueira, E., Both, C., Zeadally, S., & Rosário, D. (2022). Evaluation of an adaptive resource allocation for lorawan. Journal of Signal Processing Systems, 94(1), 65–79. https://doi.org/10.1007/s11265-021-01678-8
- [10] Urabe, I., Li, A., Fujisawa, M., Kim, S.J., & Hasegawa, M. (2023). Combinatorial mab-based joint channel and spreading factor selection for lora devices. Sensors, 23(15), 6687. https://doi.org/10.3390/s23156687
- [11] Atadet, L. A., Musabe, R., Hitimana, E., & Gatera, O. (2025). Optimizing LoRaWAN performance through learning automata-based channel selection. Future internet, 17(12), 555. https://doi.org/10.3390/fi17120555
- [12] Khawam, K., Fawaz, H., Lahoud, S., Maillard, O.A., & Martin, S. (2022). A channel selection game for multi-operator LoRaWAN deployments. Computer Networks, 216, 109185. https://doi.org/10.1016/j.comnet.2022.109185
- [13] Li, A., Fujisawa, M., Urabe, I., Kitagawa, R., Kim, S. J., & Hasegawa, M. (2021). A lightweight decentralized reinforcement learning based channel selection approach for high-density LoRaWAN. 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN) (pp. 9-14). IEEE. https://doi.org/10.1109/DySPAN53946.2021.9677146
- [14] Kakuda, S., Yamazaki, Y., Katagiri, K., Fujii, T., Takyu, O., Ohta, M., & Adachi, K. (2021). Channel allocation for lorawanconsidering intra-system and inter-system interferences. 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) (pp. 01-07). IEEE. 2021 IEEE 94th vehicular technology conference (vtc2021-fall) (pp. 1–7). https://doi.org/10.1109/VTC2021-Fall52928.2021.9625176
- [15] Kufakunesu, R., Hancke, G. P., & Abu-Mahfouz, A. M. (2020). A survey on adaptive data rate optimization in lorawan: Recent solutions and major challenges. Sensors, 20(18), 5044. https://doi.org/10.3390/s20185044
- [16] Sandoval, R. M., Garcia-Sanchez, A.J., & Garcia-Haro, J. (2019). Optimizing and updating lora communication parameters: A machine learning approach. IEEE Transactions on Network and Service Management, 16(3), 884–895. https://doi.org/10.1109/TNSM.2019.2927759
- [17] Reynders, B., Wang, Q., Tuset-Peiro, P., Vilajosana, X., & Pollin, S. (2018). Improving reliability and scalability of LoRaWANs through lightweight scheduling. IEEE Internet of Things Journal, 5(3), 1830–1842. https://doi.org/10.1109/JIOT.2018.2815150
- [18] Li, S., Raza, U., & Khan, A. (2018). How agile is the adaptive data rate mechanism of LoRaWAN?. In 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 206-212). IEEE. https://doi.org/10.1109/GLOCOM.2018.8647469
- [19] Abdelfadeel, K. Q., Cionca, V., & Pesch, D. (2018). Fair adaptive data rate allocation and power control in LoRaWAN. In 2018 IEEE 19th International Symposium on" A World of Wireless, Mobile and Multimedia Networks"(WoWMoM) (pp. 14-15). IEEE. https://doi.org/10.1109/WoWMoM.2018.8449737
- [20] Mikhaylov, K., Petäjäjärvi, J., & Janhunen, J. (2017). On LoRaWAN scalability: Empirical evaluation of susceptibility to inter-network interference. In 2017 European Conference on Networks and Communications (EuCNC) (pp. 1-6). IEEE. https://doi.org/10.1109/EuCNC.2017.7980757
- [21] Haxhibeqiri, J., den Abeele, F., Moerman, I., & Hoebeke, J. (2017). LoRa scalability: A simulation model based on interference measurements. Sensors, 17(6), 1193. https://doi.org/10.3390/s17061193
- [22] den Abeele, F., Haxhibeqiri, J., Moerman, I., & Hoebeke, J. (2017). Scalability analysis of large-scale LoRaWAN networks in ns-3. IEEE Internet of Things Journal, 4(6), 2186–2198. https://doi.org/10.1109/JIOT.2017.2768498
- [23] Elshabrawy, T., & Robert, J. (2019). Capacity planning of LoRa networks with joint noise-limited and interference-limited coverage considerations. IEEE Sensors Journal, 19(11), 4340–4348. https://doi.org/10.1109/JSEN.2019.2897156
- [24] Bor, M., & Roedig, U. (2017). LoRa transmission parameter selection. In 2017 13th International conference on distributed computing in sensor systems (DCOSS) (pp. 27-34). IEEE. https://doi.org/10.1109/DCOSS.2017.10
- [25] Lodhi, M. A., Obaidat, M. S., Wang, L., Mahmood, K., Qureshi, K. I., Chen, J., & Hsiao, K.F. (2024). Tiny machine learning for efficient channel selection in LoRaWAN. IEEE Internet of Things Journal, 11(19), 30714–30724. https://doi.org/10.1109/JIOT.2023.3342277
- [26] Margelis, G., Piechocki, R., Kaleshi, D., & Thomas, P. (2015). Low throughput networks for the IoT: Lessons learned from industrial implementations. 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT) (pp. 181-186). IEEE. https://doi.org/10.1109/WF-IoT.2015.7389049
- [27] Farhad, A., & Pyun, J.Y. (2023). LoRaWAN meets ML: A survey on enhancing performance with machine learning. Sensors, 23(15), 6851. https://doi.org/10.3390/s23156851
- [28] Ayoub Kamal, M., Alam, M. M., Sajak, A. A. B., & Mohd Su’ud, M. (2023). Requirements, deployments, and challenges of LoRa technology: A survey. Computational Intelligence and Neuroscience, 2023(1), 5183062. https://doi.org/10.1155/2023/5183062
- [29] Guerrero, M., Cano, C., Vilajosana, X., & Thubert, P. (2021). Towards dependable IoT via interface selection: predicting packet delivery at the end node in LoRaWAN networks. Sensors, 21(8), 2707. https://doi.org/10.3390/s21082707
- [30] Landge, P., Singh, Y., Mohapatra, H., & Edalatpanah, S. (2025). Efficient resource management in IoT network through ACOGA Algorithm. Computer modeling in engineering & sciences, 143(2), 1661. http://dx.doi.org/10.32604/cmes.2025.065599
- [31] Fhima, H., Mahfoudhi, S., Almutiq, M., & Bouallegue, R. (2026). Accurate LoRaWAN localization in harbors: A comparative study of VMD-BiLSTM and VMD-GAT-BiLSTM for Spatiotemporal RSSI Forecasting. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3727851
- [32] Moradbeikie, A., Zare, M., Keshavarz, A., & Lopes, S. I. (2024). RSSI-based LoRaWAN dataset collected in a dynamic and harsh industrial environment with high humidity. Data in Brief, 53, 110120. https://doi.org/10.1016/j.dib.2024.110120
- [33] Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. In International Conference on Machine Learning (pp. 1321-1330). PMLR. https://proceedings.mlr.press/v70/guo17a/guo17a.pdf
- [34] Bouguera, T., Diouris, J.F., Chaillout, J.J., Jaouadi, R., & Andrieux, G. (2018). Energy consumption model for sensor nodes based on LoRa and LoRaWAN. Sensors, 18(7), 2104. https://doi.org/10.3390/s18072104
- [35] Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7(Jan), 1–30. https://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf
- [36] Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics, 65–70. https://www.jstor.org/stable/4615733