Static Frequency-Based Canonical Coding as a Zero-Overhead Compression and Data Obfuscation Method for LoRaWAN IoT Sensors
DOI:
https://doi.org/10.31861/sisiot2026.1.01019Keywords:
data compression, LoRaWAN, Internet of Things, canonical coding, payload obfuscationAbstract
This paper proposes Static Frequency-Based Canonical Coding (SFCC) and its obfuscating extension SFCC-S – lightweight lossless compression methods for numeric IoT sensor data transmitted over LoRaWAN networks. Classical adaptive compression algorithms such as Huffman coding require transmitting a frequency table or dictionary alongside the compressed payload, consuming 28 – 128 bytes of the constrained LoRaWAN SF12 payload limit of 51 bytes per packet. The proposed approach eliminates this overhead by pre-sharing a static canonical code table between the sensor node and the receiving server, directly extending the static-context philosophy of the IETF SCHC standard (RFC 9011) from network headers to application-layer payloads. SFCC assigns variable-length binary codewords to a 14-symbol numeric sensor alphabet based on expected symbol frequencies, achieving a coding efficiency of 96.3% relative to the Shannon entropy bound. When codebook overhead is included, SFCC reduces total transmitted bits by a factor of 2.97 compared to dynamic Huffman coding. Over a 24-hour IoT simulation at 60 measurements per hour, SFCC reduces the number of LoRaWAN packets by 52.4%, projecting approximately 2x improvement in battery lifetime. SFCC-S extends the base method with a zero-overhead symbol permutation that reduces the observability of payload structure; this mechanism does not provide cryptographic security but mitigates statistical and format-based information leakage in constrained LPWAN environments. Both methods require only a 32-entry static lookup table, making them suitable for any MCU-class device.
Downloads
References
O. Väänänen and T. Hämäläinen, “Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption,” Sensor Review, vol. 42, no. 5, pp. 503–516, 2022. doi: 10.1108/SR-10-2021-0360.
M. A. Almuhaya, W. A. Jabbar, N. Sulaiman, and S. Abdulmalek, “A survey on LoRaWAN technology: Recent trends, opportunities, simulation tools and future directions,” Electronics, vol. 11, no. 1, Art. no. 164, 2022. doi: 10.3390/electronics11010164.
J. A. Oliveira Júnior, E. T. de Camargo, and M. S. Oyamada, “Data compression in LoRa networks: A compromise between performance and energy consumption,” Journal of Internet Services and Applications, vol. 14, no. 1, pp. 95–106, 2023. doi: 10.5753/jisa.2023.3000.
R. L. Dias, E. C. Vilas Boas, F. A. P. de Figueiredo, S. B. Mafra, and M. Ahmed Ouameur, “Data compression in LoRa networks: Performance and energy trade-offs of classical and cutting-edge compression algorithms,” Sensors, vol. 26, no. 5, Art. no. 1414, 2026. doi: 10.3390/s26051414.
R. S. Sinha, Y. Wei, and S. H. Hwang, “A survey on LPWA technology: LoRa and NB-IoT,” ICT Express, vol. 3, no. 1, pp. 14–21, 2017. doi: 10.1016/j.icte.2017.03.004.
LoRa Alliance, “LoRaWAN Regional Parameters, RP002-1.0.3,” 2021. [Online]. Available: https://lora-alliance.org/wp-content/uploads/2021/05/RP002-1.0.3-FINAL-1.pdf
P. Gkotsiopoulos, D. Zorbas, and C. Douligeris, “Performance determinants in LoRa networks: A literature review,” IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1721–1758, 2021. doi: 10.1109/COMST.2021.3090409.
K. L. Ketshabetswe, A. M. Zungeru, B. Mtengi, C. K. Lebekwe, and S. Prabaharan, “Data compression algorithms for wireless sensor networks: A review and comparison,” IEEE Access, vol. 9, pp. 136872–136891, 2021. doi: 10.1109/ACCESS.2021.3116311.
O. Gimenez and I. Petrov, “Static context header compression and fragmentation (SCHC) over LoRaWAN,” IETF RFC 9011, 2021. [Online]. Available: https://www.rfc-editor.org/info/rfc9011
J. Sanchez-Gomez, J. Gallego-Madrid, R. Sanchez-Iborra, J. Santa, and A. F. Skarmeta, “Impact of SCHC compression and fragmentation in LPWAN: A case study with LoRaWAN,” Sensors, vol. 20, no. 1, Art. no. 280, 2020. doi: 10.3390/s20010280.
T. M. Cover and J. A. Thomas, Elements of Information Theory, 2nd ed. Hoboken, NJ, USA: Wiley-Interscience, 2006. doi: 10.1002/047174882X.
K. Sayood, Introduction to Data Compression, 5th ed. Morgan Kaufmann, 2017.
A. Moffat, “Huffman coding,” ACM Computing Surveys, vol. 52, no. 4, Art. no. 85, 2019. doi: 10.1145/3342555.
D. Salomon, Data Compression: The Complete Reference, 4th ed. Springer, 2007. doi: 10.1007/978-1-84628-603-2.
Published
Issue
Section
License
Copyright (c) 2026 Security of Infocommunication Systems and Internet of Things
This work is licensed under a Creative Commons Attribution 4.0 International License.
