Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless rechargeable sensor networks (WRSNs) face challenges in efficient data collection and energy management.
• The integration of mobile vehicles for charging and data gathering is a key strategy for WRSNs.
• Optimizing the scheduling and resource allocation for these mobile entities is crucial for network performance.

Purpose of the Study:

• To propose an efficient algorithm for scheduling dual-purpose vehicles (data collection vehicles (DCVs) and wireless charging vehicles (WCVs)) in large-scale WRSNs.
• To reduce data transmission delay and energy consumption for mobile data collectors.
• To maximize overall network data throughput.

Main Methods:

• A twice-partition algorithm based on center points for network division.
• An anchor selection algorithm (AS-NAE) balancing neighbor count and residual energy for zonal data collection.
• An optimization function to adjust node data and link rates for maximum throughput.

Main Results:

• The proposed twice-partition algorithm effectively divides large-scale WRSNs.
• The AS-NAE algorithm successfully reduces data transmission delay and DCV movement energy consumption.
• The optimization function enhances overall data throughput in the WRSN.

Conclusions:

• The developed algorithms provide an effective solution for efficient operation in WRSNs.
• The dual-vehicle approach, combined with optimized scheduling and data collection strategies, significantly improves network performance.
• Numerical simulations validate the effectiveness of the proposed methods in WRSNs.