LoRa-Based Smart Boat-to-Land Emergency Communication Using Multi-Microcontroller Architecture
DOI:
https://doi.org/10.15662/IJEETR.2026.0802454Keywords:
LoRa, maritime emergency communication, multi-hop relay protocol, GPS tracking, ADXL345 accelerometer, dual-microcontroller architecture, chirp spread spectrum, low-power embedded systems, artisanal fishing safety.Abstract
India’s approximately 7,500 km coastline supports a fishing workforce of over four million artisanal fishermen who regularly operate 20–40 km offshore, well beyond the reliable coverage boundary of terrestrial cellular networks. When life-threatening incidents occur at such distances—vessel capsizing, sudden medical emergencies, or mechanical failure—the complete absence of a functional communication channel transforms otherwise survivable situations into fatalities. This paper presents the design, laboratory characterisation, and live coastal field evaluation of a LoRa-based emergency alerting system that functions without SIM cards, internet connectivity, or recurring subscription costs. The system defines three distinct hardware roles: a Boat Unit equipped with a u-blox NEO-6M GPS receiver, an ADXL345 three-axis MEMS accelerometer, and a large-format latching emergency push button; an intermediate Relay Node executing packet-forwarding firmware on hardware-compatible platforms; and a shore-based Land Monitoring Unit. A dual-microcontroller architecture partitions the Boat Unit into a dedicated sensor-acquisition subsystem and an independent radio-management subsystem, eliminating the SPI bus contention that caused missed sensor events in single-MCU prototype iterations. Distress packets are conveyed over LoRa at 433 MHz via a purpose-designed application-layer multi-hop relay protocol incorporating per-hop CRC-16 validation, sequence-number-based deduplication, and randomised transmission back-off. Vessels beyond direct communication range are served automatically by neighbouring relay-equipped boats, each forwarding packets shoreward and extending effective coverage by 10–12 km per hop. Over two days of coastal field trials spanning six range configurations and varying sea states, a two-hop relay chain covering 27 km sustained a packet delivery rate (PDR) of 94.1% at a median end-to-end alert latency of 3.24 seconds. The complete Boat Unit was fabricated for under INR 4,500 and sustains continuous operation for more than 72 hours from a sealed 5 Ah lead-acid battery. These results confirm that infrastructure-independent, long-range maritime emergency communication is practically achievable at a cost point accessible to artisanal fishing communities worldwide.
References
1. J. Park, S. Kim, and H. Lee, "LoRa-based maritime emergency communication system for small vessels," IEEE Access, vol. 7, pp. 145321–145330, 2019. doi: 10.1109/ACCESS.2019.2942513
2. S. Kumar, R. Singh, and A. Verma, "IoT-based marine safety and monitoring system using GSM and GPS," IEEE Internet Things J., vol. 7, no. 5, pp. 4001–4010, May 2020. doi: 10.1109/JIOT.2019.2958721
3. R. Sharma and P. Mehta, "Wireless distress alert system for fishermen using embedded technology," IEEE Sensors J., vol. 18, no. 12, pp. 5023–5031, Jun. 2018. doi: 10.1109/JSEN.2018.2830057
4. Silva, J. Ferreira, and M. Almeida, "Low power wide area networks for maritime applications," IEEE Commun. Mag., vol. 59, no. 3, pp. 72–78, Mar. 2021. doi: 10.1109/MCOM.001.2000394
5. P. Nguyen, T. Tran, and H. Vo, "Performance evaluation of LoRa technology for marine communication," IEEE Trans. Veh. Technol., vol. 69, no. 9, pp. 10345–10355, Sep. 2020. doi: 10.1109/TVT.2020.3007059
6. M. Lee and K. Park, "Smart boat safety monitoring system using IoT and GPS," IEEE Access, vol. 10, pp. 55112–55121, 2022. doi: 10.1109/ACCESS.2022.3175890
7. K. Rao and D. Reddy, "Emergency alert system using wireless sensor networks for remote areas," in Proc. IEEE Int. Conf. Commun. Syst. (ICCS), Shenzhen, China, 2017, pp. 210–215.
8. H. Chen, Y. Wang, and X. Li, "IoT-based coastal safety and emergency management system," IEEE Sensors Lett., vol. 5, no. 8, pp. 1–4, Aug. 2021. doi: 10.1109/LSENS.2021.3094477
9. V. Patel and S. Shah, "Real-time embedded system for boat safety and emergency communication," in Proc. IEEE Int. Conf. Embedded Syst. (ICES), Bengaluru, India, 2019, pp. 98–103.
10. L. Wang, Z. Liu, and Y. Zhang, "Design of a low-cost IoT-based emergency communication system for remote environments," IEEE Access, vol. 10, pp. 87456–87465, 2022. doi: 10.1109/ACCESS.2022.3199654
11. Semtech Corporation, "SX1276/77/78/79 datasheet: 137 MHz to 1020 MHz low power long range transceiver," Rev. 7, Semtech, Camarillo, CA, USA, 2020. [Online]. Available: https://www.semtech.com/products/wireless-rf/lora-connect/sx1276
12. M. Bor, J. E. Vidler, and U. Roedig, "LoRa for the internet of things," in Proc. Int. Workshop Low Power Wide Area Netw. Internet Things (LPWAN), Stuttgart, Germany, 2016, pp. 1–6. doi: 10.14279/depositonce-5159
13. Augustin, J. Yi, T. Clausen, and W. M. Townsley, "A study of LoRa: Long range and low power networks for the internet of things," Sensors, vol. 16, no. 9, p. 1466, Sep. 2016. doi: 10.3390/s16091466
14. Goursaud and J.-M. Gorce, "Dedicated networks for IoT: PHY/MAC state of the art and challenges," EAI Endorsed Trans. Internet Things, vol. 1, no. 1, pp. 1–11, Oct. 2015. doi: 10.4108/eai.26-10-2015.150597
15. N. Varsier and J. Schwoerer, "Capacity limits of LoRaWAN technology for smart metering applications," in Proc. IEEE Int. Conf. Commun. (ICC), Paris, France, 2017, pp. 1–6. doi: 10.1109/ICC.2017.7997098
16. IEEE Std 802.15.4-2020, "IEEE standard for low-rate wireless networks," IEEE, New York, NY, USA, 2020. doi: 10.1109/IEEESTD.2020.9144691
17. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
18. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
19. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
20. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
21. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
22. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
23. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- ActaElectrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
24. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
25. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
26. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
27. SuganthiMullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, RevistaMateria (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
28. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
29. Anand, L., Maurya, M., Seetha, J., Nagaraju, D., Ravuri, A., &Vidhya, R. G. (2023, July). An intelligent approach to segment the liver cancer using Machine Learning Method. In 2023 4th international conference on electronics and sustainable communication systems (ICESC) (pp. 1488-1493). IEEE.
30. Rajendran, S., Sundarapandi, A. M. S., Krishnamurthy, A., &Thanarajan, T. (2022). An intelligent face recognition technology for iot-based smart city application using condition-cnn with foraging learning pso model. International Journal of Pattern Recognition and Artificial Intelligence, 36(14), 2256018.
31. Murugeshwari, B., &Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
32. Sugumar, R. (2025). Unified AI Framework for Predictive Data Engineering and Real Time Prescription and Billing Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(5), 17261.
33. Samrat, B., Thomas, P. K., Kumar, S., Benila, A., Bhardwaj, R., &Vigenesh, M. (2024, December). Industrial informatics in optimizing software-defined vehicles for logistics. In 2024 IEEE 2nd International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP) (pp. 1-9). IEEE.
34. Soundappan, S. J. (2024). AI-driven customer intelligence in enterprise lakehouse systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology.
35. Rajasekar, M. (2024). AI-Powered Cyber-Secure Federated Learning on AWS for Next-Generation Digital Banking Analytics. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(3).
36. Deivendran, P., Babu, P. S., Malathi, G., Anbazhagan, K., & Kumar, R. S. (2023). Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network. arXiv preprint arXiv:2305.06842.
37. Sugumar, R., &Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
38. Pandey, V. K., Mishra, S., Rengarajan, A., Savita, &Roomi, M. M. (2024, March). Enhancing Weather Forecasting with Machine Learning Techniques. In International Conference on Renewable Power (pp. 147-156). Singapore: Springer Nature Singapore.
39. Mathew, A., & Alex, H. (2025). Federated Learning for Secure Genomic Research: Privacy-Preserving AI Solutions for Precision Medicine. Science and Technology: Developments and Applications Vol. 9, 36-43.
40. Selvi, G. V., Anbarasan, A. B., Murthy, B. A., &Prabavathy, S. (2023). An Application Oriented Integrated Unequal Clustering Algorithm for Wireless Sensor Network. In Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques (pp. 140-154). CRC Press.
41. Soundappan, S. J. (2025). Next Generation AI Enabled Holistic Cognitive Platform for Secure Cloud Network Intelligence Enterprise Systems and Digital Trust Optimization. International Journal of Computer Technology and Electronics Communication, 8(5), 11534-11542.
42. Rajasekar, M. (2024). Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10713-10718.
43. Jagadeesh, S., & Sugumar, R. (2017). A comparative study on artificial bee colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243–248.
44. Murugeshwari, B., Sarukesi, K., &Jayakumar, C. (2010, March). An efficient method for knowledge hiding through database extension. In 2010 International Conference on Recent Trends in Information, Telecommunication and Computing (pp. 342-344). IEEE.
45. Reddy, K. V. V. K., &Vimal, V. R. (2024, July). A novel approach on improved segmentation and classification of remote sensing images using AlexNet compared over linear discriminant analysis with improved accuracy. In 2024 Second International Conference on Advances in Information Technology (ICAIT) (Vol. 1, pp. 1-6). IEEE.
46. Gowthami, D., &Vigenesh, M. (2024). Distributed and Lightweight Intrusion Detection for IoT: A Lightweight Pyramidal U-Net With Tri-Level Dual Inception-Based Framework. In The Convergence of Self-Sustaining Systems With AI and IoT (pp. 154-173). IGI Global Scientific Publishing.
47. Anand, P. V., &Anand, L. (2023, December). An Enhanced Breast Cancer Diagnosis using RESNET50. In 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-5). IEEE.
48. Mathew, A. (2022). Leveraging Big Data Analytics to Power AI and ML (Machine Learning) Automation. Educational Research (IJMCER), 4(5), 131-134.
49. Dhinakaran, D. (2022). Joe Prathap P. M, Selvaraj D, Arul Kumar D and Murugeshwari B," Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing,". International Journal of Engineering Trends and Technology, 70(3), 284-294.
50. Poornima, G., &Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
51. Rengarajan, A., Jayakumar, C., & Sugumar, R. (2012). Optimization Of Recent Attacks Using Internet Protocol. National Journal of System and Information Technology, 5(1), 8.
52. Mathew, A., &Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154-164.
53. Vekariya, V., Kumar, S., &Rengarajan, A. (2024). A distinctive and smart agricultural knowledge-based framework using ontology. In Sustainability in Digital Transformation Era: Driving Innovative & Growth (pp. 207-213). CRC Press.
54. Soundappan, S. J. (2020). Big data analytics in healthcare: Applications for pandemic forecasting. International Journal of Advanced Research in Computer Science & Technology, 3.
55. Sugumar, R. (2024). AI-Augmented Quality Engineering for Performance Optimization and Test Orchestration in Distributed Systems. International Journal of Science, Research and Technology, 7(5), 12835-12846.
56. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338–356.
57. Mathew, A. (2025). Ahead of the breach: Predictive threat intelligence in aviation inspired by Scattered Spider attacks. Multidisciplinary International Journal of Research and Development (MIJRD), 4(6), 54–58.
58. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
59. Garg, V. K., Soundappan, S. J., &Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64.
60. Anand, L., Tyagi, R., & Mehta, V. (2024, January). Food recognition using deep learning for recipe and restaurant recommendation. In Proceedings of Eighth International Conference on Information System Design and Intelligent Applications (pp. 269-279). Singapore: Springer Nature Singapore.
61. Kumar, A., &Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII Transactions on Internet and Information Systems (TIIS), 19(11), 3841-3855.
62. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
63. Chandra, S., Rengarajan, A., Sahoo, G. S., & Sharma⁴, S. (2024, October). Identifying Neuronal Damage and Plasticity by Analyzing Changes in Diffusion Tensor. In Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 2: ICDSMLA 2023, 15–16 December, Hyderabad, India (Vol. 2, p. 433). Springer Nature.
