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Article

Securing Marine Assets: Edge ML and LoRa Mesh Integration for IoT Anti-Theft Systems

by
Damiano Vincenzo Coppola
,
Miriana Russo
*,
Corrado Santoro
,
Federico Fausto Santoro
*,
Angelo Spadola
and
Alessio Tudisco
Department of Mathematics and Informatics, University of Catania, Via Santa Sofia 6, 95125 Catania, Sicily, Italy
*
Authors to whom correspondence should be addressed.
IoT 2026, 7(3), 79; https://doi.org/10.3390/iot7030079
Submission received: 19 May 2026 / Revised: 25 August 2026 / Accepted: 3 September 2026 / Published: 9 September 2026
(This article belongs to the Special Issue Advances in Intelligent Wireless Sensing and IoT)
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Abstract

This paper presents a maritime Internet of Things anti-theft architecture based on an ESP32 onboard node, local motion analysis, and LoRa mesh communication. The proposed detection pipeline uses a one-dimensional convolutional autoencoder trained on stationary vessel data and applied to sliding windows of X- and Z-axis angular velocity measurements. During deployment, a calibration phase estimates the reconstruction-error threshold from the local motion profile of the moored vessel, reducing the dependence on labelled theft examples. The communication layer combines an Elliptic Curve Cryptography setup phase with symmetric payload encryption for alert packets, while ESP32 hardware security features are used to protect firmware and stored credentials. The mesh network uses controlled flooding. A Godot-based simulation environment was used to generate stationary and towing scenarios under different wave configurations. In the current simulation campaign, towing windows produced a higher mean reconstruction error than stationary windows. The results support the feasibility of the architecture and also show that event-level alert logic is required to aggregate window-level anomaly scores into reliable alarms.
Keywords: edge ML; embedded security; LoRa mesh; maritime IoT; machine learning edge ML; embedded security; LoRa mesh; maritime IoT; machine learning

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MDPI and ACS Style

Coppola, D.V.; Russo, M.; Santoro, C.; Santoro, F.F.; Spadola, A.; Tudisco, A. Securing Marine Assets: Edge ML and LoRa Mesh Integration for IoT Anti-Theft Systems. IoT 2026, 7, 79. https://doi.org/10.3390/iot7030079

AMA Style

Coppola DV, Russo M, Santoro C, Santoro FF, Spadola A, Tudisco A. Securing Marine Assets: Edge ML and LoRa Mesh Integration for IoT Anti-Theft Systems. IoT. 2026; 7(3):79. https://doi.org/10.3390/iot7030079

Chicago/Turabian Style

Coppola, Damiano Vincenzo, Miriana Russo, Corrado Santoro, Federico Fausto Santoro, Angelo Spadola, and Alessio Tudisco. 2026. "Securing Marine Assets: Edge ML and LoRa Mesh Integration for IoT Anti-Theft Systems" IoT 7, no. 3: 79. https://doi.org/10.3390/iot7030079

APA Style

Coppola, D. V., Russo, M., Santoro, C., Santoro, F. F., Spadola, A., & Tudisco, A. (2026). Securing Marine Assets: Edge ML and LoRa Mesh Integration for IoT Anti-Theft Systems. IoT, 7(3), 79. https://doi.org/10.3390/iot7030079

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