Scalable and Trusted Metadata-Coordinated Tiered Off-Chain Storage with Dynamic On-Chain Mapping for Recovery-Safe and Low-Latency IoT Data Management

Blockchain-assisted off-chain storage for IoT must simultaneously manage low-latency tiered data placement, trusted and dynamic on-chain mapping, migration consistency, and failure recovery—four concerns that existing designs address in isolation. Tiered storage systems optimize placement without modeling the scalable coordination cost of keeping object–location bindings trustworthy, while blockchain-metadata studies assume static storage topologies with no dynamic tier migration. This paper presents a scalable and trusted metadata-coordinated tiered off-chain storage framework, which bridges traditional trust systems (e.g., legacy authentication) with blockchain networks powered by Proof of Capacity (PoC) consensus. In this framework, adaptive heat-driven placement, dynamic on-chain mapping evolution with batched commitment, migration-aware redirect control, and rollback-safe recovery operate as a single coordinated workflow, with the five-stage write–verify–commit–redirect–retire pipeline acting as a lightweight coordination protocol that maintains ordered and atomic state transitions under message loss, out-of-order delivery, and single-node failures. The distinctive contribution lies in the framework’s coupled control: every placement decision propagates through a verifiable metadata path that can be audited and, when necessary, rolled back. Simulation across multiple workload patterns shows that the proposed method reduces average access latency by 28% and raises the hot-tier hit ratio from 0.19 to 0.65 relative to a dynamic baseline without trusted mapping coordination under the simulated registry write cost. To achieve high-throughput mapping operations, batched on-chain commitment cuts metadata transactions by 50× at the cost of a tunable mapping freshness delay. The framework scales from 1 k to 50 k managed objects, effectively managing tens of millions of bytes of data (10+ MB scale) without disproportionate overhead growth; beyond this scale, hot-tier capacity rather than coordination becomes the dominant bottleneck, and smarter predictive placement becomes the natural next lever. All tested fault types achieve 100% rollback success with sub-millisecond local data plane interruption; audit-visible recovery depends on the assumed chain finality delay and, for heavily regulated IoT domains, such as finance and healthcare, should be treated as the operationally binding recovery time objective. These results, together with extended evaluations—including asymmetric write latency stress, coordination ablation, tail latency analysis, and benefit–complexity assessment—provide quantitative evidence that scalable, dynamic mapping coordination can be integrated into tiered off-chain data management at an acceptable and measurable operational cost under the simulated configuration.