Lightweight Tamper-Evident Log Integrity Verification for IoT Edge Environments: A Merkle Tree Pipeline with Adaptive Chunking

• URL: http://arxiv.org/abs/2605.00065v1
• Date: Thu, 30 Apr 2026 09:16:43 GMT
• Title: Lightweight Tamper-Evident Log Integrity Verification for IoT Edge Environments: A Merkle Tree Pipeline with Adaptive Chunking
• Abstract summary: Integrity of audit logs produced by Internet of Things (IoT) devices is a prerequisite for post-incident forensics, regulatory compliance, and operational accountability.<n>This paper presents a lightweight and evaluated integrity verification pipeline that combines Merkle-tree commitments with resource-aware adaptive chunking.<n>The proposed pipeline operates in three stages: (i) resource-aware batch ingestion via adaptive chunk sizing, (ii) Merkle-tree construction with O(logn) inclusion proof generation, and (iii) deterministic single-entry verification against a trusted root anchor.
• Score: 0.4779196219827507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integrity of audit logs produced by Internet of Things (IoT) devices is a prerequisite for post-incident forensics, regulatory compliance, and operational accountability. While blockchain-backed logging infrastructures can satisfy this requirement, they introduce consensus overhead, network dependencies, and deployment complexity that are often prohibitive at the IoT edge. This paper presents a lightweight and evaluated integrity verification pipeline that combines Merkle-tree commitments with resource-aware adaptive chunking to provide tamper evidence without relying on distributed ledger technologies. The proposed pipeline operates in three stages: (i) resource-aware batch ingestion via adaptive chunk sizing, (ii) Merkle-tree construction with O(logn) inclusion proof generation, and (iii) deterministic single-entry verification against a trusted root anchor. We further report an implementation audit that identified and corrected two evaluation defects: a double-counting bug in tampering metrics and a redundant full-tree reconstruction during batch appends. Using the corrected implementation, five-run benchmarks on synthetic IoT log datasets demonstrate throughput exceeding 130,000 logs/s for 100,000 records. The system achieves per-entry verification latency of approximately 22 ms, proof generation latency of 22 ms, an average proof size of 1,006 bytes, and peak memory usage below 5 MB. Tampering detection achieves perfect precision, recall, and F1-score (1.0) across corruption ratios ranging from 1% to 50%.

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