Information-Based Smart RF Energy Harvesting in Wireless Sensor Networks
Information-Based Smart RF Energy Harvesting in Wireless Sensor Networks
This project, which formed the basis of my Master’s thesis at UNC Charlotte, focused on developing information-based approaches to optimize RF energy harvesting in wireless sensor networks. The research explored how information theory can be applied to improve energy efficiency in resource-constrained wireless environments.
Project Highlights
- Developed novel information-based approaches for RF energy harvesting in wireless sensor networks
- Created mathematical models to optimize energy harvesting based on information theory principles
- Implemented and tested the approaches using software-defined radios (USRP)
- Evaluated performance improvements compared to traditional energy harvesting methods
- Published research findings in IEEE conference proceedings
Impact
This research contributes to the field of energy-efficient wireless sensor networks, which are critical for IoT applications, environmental monitoring, and smart cities. By optimizing energy harvesting based on information theory, the approach helps extend the operational lifetime of wireless sensor networks while maintaining communication performance.
Related Publications
Technologies Used
- Software-defined radios (USRP)
- MATLAB for simulation and analysis
- Information theory principles
- Wireless sensor network protocols
- Energy harvesting circuits and techniques