Drift-Adaptive Synchronization for Energy–Fidelity Tradeoffs in Digital Twin IoT Systems

Abstract

Digital twin systems require continuous synchronization with distributed Internet of Things devices to maintain accurate representations of physical processes, but frequent updates incur significant energy and latency overhead. Conventional strategies rely on static parameters that do not adapt to time-varying dynamics, leading to inefficient resource usage or degraded fidelity. This article introduces a unified evaluation framework integrating energy consumption, reconstruction accuracy, and latency through the energy-aware twin update for networked environments metric, and proposes a drift-adaptive synchronization mechanism that dynamically adjusts update thresholds based on observed signal drift. The energy model is grounded in the Si4460 transceiver hardware of the evaluation dataset, where transmission energy dominates the energy consumed during deep-sleep operation, validating the use of a per-transmission energy proxy across all policies. Evaluation on real-world crowd-sensing traces and synthetic workloads shows that the adaptive mechanism achieves favorable energy–fidelity–freshness tradeoffs under moderate dynamics and remains competitive across static and highly dynamic operating regimes. Age of Information (AoI) analysis further shows that the adaptive policy achieves the lowest mean staleness, reducing AoI by 55% relative to periodic synchronization. These results demonstrate that drift-adaptive synchronization provides an effective and lightweight solution for energy-efficient digital twin systems.