# A Hybrid Hash–Encryption Scheme for Secure Transmission and Verification of Marine Scientific Research Data

**Authors:** Hanyu Wang, Mo Chen, Maoxu Wang, Min Yang

PMC · DOI: 10.3390/s26030994 · Sensors (Basel, Switzerland) · 2026-02-03

## TL;DR

This paper introduces a secure data transmission framework for marine research that works well over unstable connections.

## Contribution

A novel hybrid encryption-verification scheme is proposed for secure and verifiable data transmission in marine environments.

## Key findings

- The framework reduces storage and encapsulation overhead compared to traditional methods like SHA-256 + RSA + AES.
- Verification latency scales linearly with block count and throughput remains stable under fragmentation.
- The system performs efficiently on resource-constrained devices like Raspberry Pi 4.

## Abstract

Marine scientific observation missions operate over disrupted, high-loss links and must keep heterogeneous sensor, image, and log data confidential and verifiable under fragmented, out-of-order delivery. This paper proposes an end-to-end encryption–verification co-design that integrates HMR integrity structuring with EMR hybrid encapsulation. By externalizing block boundaries and maintaining a minimal receiver-side verification state, the framework supports block-level integrity/provenance verification and selective recovery without continuous sessions, enabling multi-hop and intermittent connectivity. Experiments on a synthetic multimodal ocean dataset show reduced storage/encapsulation overhead (10.4% vs. 12.8% for SHA-256 + RSA + AES), lower hashing latency (6.8 ms vs. 12.5 ms), and 80.1 ms end-to-end encryption–decryption latency (21.2% lower than RSA + AES). Under fragmentation, verification latency scales near-linearly with block count (R2 = 0.998) while throughput drops only slightly (11.8 → 11.3 KB/ms). With 100 KB blocks, transmission latency stays below 1.024 s in extreme channels and around 0.08–0.10 s in typical ranges, with expected retransmissions < 0.25. On Raspberry Pi 4, runtime slowdown remains stable at ~3.40× versus a PC baseline, supporting deployability on resource-constrained nodes.

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12899856/full.md

## References

30 references — full list in the complete paper: https://tomesphere.com/paper/PMC12899856/full.md

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Source: https://tomesphere.com/paper/PMC12899856