Enabling Semantic-Functional Communications for Multiuser Event Transmissions via Wireless Power Transfer.

Area of Science:

• Electrical Engineering
• Computer Science
• Wireless Communication

Background:

• Large-scale sensor networks and the Internet of Things (IoT) face challenges with battery capacity and recharging.
• Radio frequency-based energy harvesting (RF-EH) offers a solution for low-power networks where traditional charging is impractical.
• Existing methods treat energy harvesting separately from data transmission and reception, limiting efficiency.

Purpose of the Study:

• To propose a novel method for recovering information from battery charge in sensor networks.
• To design an event-driven sensor network utilizing semantic-functional communication and RF-EH.
• To enable simultaneous battery charging and information decoding.

Main Methods:

• Developed a semantic-functional communication framework for sensor networks.
• Integrated radio frequency-based energy harvesting (RF-EH) for battery recharging.
• Investigated system performance using metrics like event signaling, detection, battery status, signaling success rates, and Age of Information (AoI).

Main Results:

• Demonstrated a system where information can be recovered from battery charge.
• Evaluated the effectiveness of RF-EH in an event-driven sensor network.
• Analyzed the relationship between key parameters and system behavior through a case study.

Conclusions:

• The proposed method enables efficient information recovery and battery charging in sensor networks.
• The integration of semantic-functional communication with RF-EH addresses critical IoT power challenges.
• Numerical results validate the effectiveness and feasibility of the developed system.