Adaptive Control of Merkle Epoch Commitment for Ensuring Telemetry Integrity in Industrial IoT Systems

Abstract

Protecting telemetry in Industrial Internet of Things systems requires reliable data aggregation at gateway nodes. Merkle trees provide a compact mechanism for verifying the inclusion of events in a dataset. However, a static epoch size is poorly suited to variable workloads: large epochs increase external commitment latency, whereas small epochs increase system overhead. This paper proposes an adaptive algorithm for forming Merkle epochs that dynamically adjusts the size of the aggregated data block. The algorithm considers the intensity of the incoming data stream, external storage availability and performance, gateway resource constraints, memory utilization, event criticality, and the anomaly level of observed parameters. The key metric is commitment latency, defined as the time interval between the arrival of an event at the gateway and confirmation that the corresponding Merkle root has been recorded in external storage. Simulation results show that the proposed multi-factor algorithm automatically adapts to changing system conditions and mitigates failure risks under abrupt workload surges.