Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) are transitioning to wireless rechargeable sensor networks (WRSNs) due to wireless power transfer (WPT) advancements.
• Existing research primarily focuses on charging efficiency, neglecting routing optimization in WRSNs.

Purpose of the Study:

• To develop a joint optimization model for maximizing charging efficiency and routing structure in WRSNs.
• To address the gap in routing optimization for wireless rechargeable sensor networks.

Main Methods:

• Decomposition of the optimization model and proposal of a heuristic algorithm for optimal charging efficiency with a predefined routing tree.
• Application of a genetic algorithm (GA) with specialized tree-based recombination and mutation for joint routing and charging optimization.
• Coding the many-to-one communication topology as an individual for the genetic algorithm.

Main Results:

• The heuristic algorithm effectively reduces the number of resident locations and the total moving distance.
• The proposed joint optimization approach significantly enhances charging efficiency compared to existing methods.
• Genetic algorithm demonstrates fast convergence through tailored genetic operations.

Conclusions:

• The developed joint optimization model and algorithms offer a significant improvement in WRSN performance.
• This work provides an effective solution for enhancing both energy management and network topology in WRSNs.
• The findings pave the way for more efficient and robust wireless rechargeable sensor networks.