Fuzzy energy management strategies for energy harvesting IoT nodes based on a digital twin concept

Abstract

This study presents a cloud-assisted energy management strategy for energy harvesting Internet-of-Things (IoT) nodes, using a novel digital twin (DT) concept for dynamic optimization of IoT node behavior. The system is built upon a fuzzy-rule-based controller optimized through a differential evolution (DE) algorithm. DE is particularly well-suited for this application, as it is capable of optimizing the controller without requiring gradient information, allowing it to efficiently navigate the complex, nonlinear characteristics of IoT energy management problems. The optimization process tunes nine key fuzzy input coefficients to create an energy-efficient control strategy. The DT concept serves as a virtual replica of the physical IoT node, continuously synchronizing real-time data from sensors and other internal parameters, including energy harvesting rates and component health. Through this real-time feedback loop, the DT enables predictive adjustments to the control system, increasing the longevity and reliability of the IoT devices in harsh and changing environments. Compared to traditional energy management strategies, the proposed method improves energy utilization by 11%, leveraging four years of solar data collected from multiple geographical locations. Moreover, the system achieves a 12% increase in successful transmissions, ensuring greater data availability in the cloud while minimizing device failures and optimizing the use of available energy. The DT concept allows the system to simulate and predict IoT node behavior under various conditions, continuously refining the energy management strategy. This ensures not only optimal energy efficiency but also accounts for component degradation over time, offering long-term adaptability and minimizing the need for manual intervention. Thus, the synergy between the DT concept and DE optimization offers a powerful, scalable solution for managing energy-constrained IoT networks, surpassing conventional expert-designed strategies in both adaptability and performance.