On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance

Area of Science:

• Energy Harvesting
• Wireless Sensor Networks
• Sustainable Computing

Background:

• The Internet of Things (IoT) requires numerous sensors, posing challenges for battery maintenance and disposal.
• Battery-free sensor platforms using supercapacitors offer a sustainable alternative, but mobility effects are understudied.
• Existing research on supercapacitors for IoT has overlooked the impact of node movement on energy storage and application performance.

Purpose of the Study:

• To investigate the critical role of supercapacitor size in mobile battery-free sensor systems.
• To address the trade-offs between capacitor size, charging speed, and support for energy-intensive tasks.
• To propose and evaluate a novel hybrid storage solution for optimizing performance in dynamic energy environments.

Main Methods:

• Investigated the impact of supercapacitor sizing on mobile sensor performance.
• Developed a hybrid energy storage system utilizing an adaptive learning algorithm.
• Dynamically switched between two capacitors based on predicted ambient energy availability.
• Evaluated the system using extensive simulations and prototype measurements.

Main Results:

• Supercapacitor size significantly impacts mobile system performance and task support.
• The proposed hybrid storage solution dynamically adapts to varying energy conditions.
• Achieved up to 40% improvement in harvested energy compared to fixed-capacitor approaches.
• Demonstrated up to 80% improvement in sensor coverage with the adaptive system.

Conclusions:

• Optimizing supercapacitor size is non-trivial for mobile battery-free IoT applications.
• A hybrid, adaptive storage approach effectively manages energy fluctuations.
• The proposed solution enhances the reliability and efficiency of perpetual IoT sensors.