Digital Twin-Assisted Chance-Constrained Energy-Aware Scheduling for Self-Sustainable IIoT Networks

Abstract

On-demand data sensing and Wireless Power Transfer (WPT) enable sustainable operation in large-scale Industrial Internet of Things (IIoT) networks. Existing scheduling frameworks are inherently reactive, initiating charging only after an IoT node’s residual energy falls below a predefined threshold, which increases the risk of energy outages and service disruption. This paper proposes a Digital Twin (DT)-enabled proactive scheduling framework for self-sustainable IIoT networks under stochastic operating conditions. The proposed framework integrates three key components. First, a DT layer continuously mirrors IoT node states and predicts future energy availability over a finite prediction horizon. Second, a predictive charging strategy proactively schedules WPT before energy depletion occurs. Third, physical-layer security and DT-based anomaly detection protect the network against eavesdropping, false-data injection, and energy depletion attacks. Fourth, the scheduling framework is reformulated using chance constraints to explicitly account for uncertainty in wireless channels, energy consumption, and DT prediction errors while providing probabilistic reliability guarantees. The resulting sensing and WPT scheduling problems are formulated as multi-slot integer optimization problems and solved using branch-and-bound, with a low-complexity greedy heuristic for latency-sensitive deployments. Simulation results show that the proposed framework reduces energy outage events to below 1%, maintains reliable operation under increasing energy uncertainty with only a modest sensing-utility reduction, and incurs negligible computational overhead compared with the deterministic formulation.