Altaf, KainatKainatAltafAli, MominMominAliHarms, LauraLauraHarmsRenner, ChristianChristianRennerLandsiedel, OlafOlafLandsiedel2026-07-242026-07-242026-0341st ACM/SIGAPP Symposium on Applied Computing, SAC 2026https://hdl.handle.net/11420/64010Real-time analysis of underwater acoustic signals is essential for maritime security, environmental monitoring, and wildlife protection. In many real-world deployments, such as remote sensing buoys, autonomous underwater vehicles, and seabed monitoring stations, data must be processed directly on the device due to limited connectivity and energy constraints. These platforms are, for example, tasked with monitoring vessel activity in ecologically sensitive zones, where timely and localized analysis is essential to detect unauthorized presence, enforce protection boundaries, and mitigate acoustic disturbances. Existing approaches mainly focus on coarse ship-type classification or perform individual ship identification at a very limited scale, often restricted to just a handful of vessels. Further, they typically rely on large cloud-based DNNs making them unsuitable for deployment on constrained devices.To close this gap, we present ShipNN, a resource-efficient DNN model for real-time ship identification from underwater acoustic signals, deployable on resource-constrained microcontrollers. ShipNN achieves 94.1% accuracy in individual ship identification, outperforming, for example, ResNet, a much larger baseline model with merely 92.2% accuracy. ShipNN has 32× fewer parameters, 17× less RAM, and 66× less flash storage than this baseline, demonstrating efficient, real-time operation on off-the-shelf microcontrollers.enhttps://creativecommons.org/licenses/by/4.0/CQTIoT devicesMFCCship identificationTinyMLunderwater acousticsvessel identificationComputer Science, Information and General Works::006: Special computer methodsConference Paper10.1145/3748522.377978710.15480/882.17574