What question did this study set out to answer?

To establish a secure provenance architecture for tactical RF spectrum monitoring systems.

March 27, 2026Open Access

Cryptographic Provenance for Streaming RF Sensor Data Using Segmented Merkle Trees with Merkle Mountain Range Aggregation

Key Points

To establish a secure provenance architecture for tactical RF spectrum monitoring systems.
Developed a segmented Merkle tree structure with 256-record segments using SHA-256.
Implemented Merkle Mountain Range aggregation for efficient inclusion proofs.
Used ECDSA P-384 for device identity binding and MessagePack for deterministic hashing.
Designed a distributed merge protocol to support data recovery with cross-anchor records.
Created evidence package exports for offline verification.
Achieved efficient data provenance with O(log n) inclusion proofs.
Enabled secure device identity binding through ECDSA signing.
Facilitated structured event categorization in RF sensor systems.
Successfully implemented a recovery protocol for data integrity.

Abstract

Defensive disclosure establishing prior art for a provenance architecture applied to tactical RF spectrum monitoring systems. The architecture combines segmented Merkle trees (256-record segments with SHA-256), Merkle Mountain Range aggregation for O(log n) inclusion proofs, ECDSA P-384 signed checkpoints for device identity binding, canonical MessagePack encoding for deterministic record hashing, an eleven-type event taxonomy for RF sensor system events, a distributed merge protocol for DDIL recovery with cross-anchor records, and a self-contained evidence package export with standalone offline verification. Implemented on ARM edge hardware (Raspberry Pi 5) for tactical spectrum monitoring.

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Isaiah Majok Dau

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Alliance Bioversity International - CIAT

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Cryptographic Provenance for Streaming RF Sensor Data Using Segmented Merkle Trees with Merkle Mountain Range Aggregation

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Abstract

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