RIoT Digital Twin: Modeling, Deployment, and Optimization of Reconfigurable IoT System with Optical-Radio Wireless Integration

TL;DR

This work tackles the energy sustainability and configurability challenge of hybrid RF/OWC IoT in the 6G era by introducing a high-fidelity Digital Twin implemented in NS-3, calibrated with real hardware energy measurements. It proposes a proactive cross-layer optimization, EUNO, that selects communication modality (BLE or VLC) and operating mode (Performance, Conservation, Sleep) via a unified utility function that accounts for residual energy, QoS, and predicted consumption. The key contributions include hardware-calibrated energy models for BLE and OWC, a modular NS-3 DT embracing OWC/BLE fusion with energy harvesting, and a low-complexity, online optimization framework that outperforms baseline thresholds in both energy efficiency and data rates. The results demonstrate substantial energy savings and resilience in dynamic, multi-modal IoT networks, illustrating the practical impact of combining DT-based modeling with hybrid optical–radio interfaces for sustainable 6G IoT deployments.

Abstract

This paper proposes an optimized Reconfigurable Internet of Things (RIoT) framework that integrates optical and radio wireless technologies with a focus on energy efficiency, scalability, and adaptability. To address the inherent complexity of hybrid optical-radio environments, a high-fidelity Digital Twin (DT) is developed within the Network Simulator 3 (NS-3) platform. The DT models deploy subsystems of the RIoT architecture, including radio frequency (RF) communication, optical wireless communication (OWC), and energy harvesting and consumption mechanisms that enable autonomous operation. Real-time energy and power measurements from target hardware platforms are also incorporated to ensure accurate representation of physical behavior and enable runtime analysis and optimization. Building on this foundation, a proactive cross-layer optimization strategy is devised to balance energy efficiency and quality of service (QoS). The strategy dynamically reconfigures RIoT nodes by adapting transmission rates, wake/sleep scheduling, and access technology selection. Results demonstrate that the proposed framework, combining digital twin technology, hybrid optical-radio integration, and data-driven energy modeling, substantially enhances the performance, resilience, and sustainability of 6G IoT networks.

Figures (17)

• Figure 1: The considered RIoT system.
• Figure 2: The developed architecture of the RIoT node in NS-3.
• Figure 3: Validation of the implemented GFSK modulation in NS-3 through BER versus SNR comparison with MATLAB reference results.
• Figure 4: Finite State Machine representation of the communication interfaces: (a) OWC, and (b) BLE.
• Figure 5: Overview of the energy framework developed in NS-3.