A Joint Energy Replenishment and Data Collection Strategy in Heterogeneous Wireless Rechargeable Sensor Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless rechargeable sensor networks rely on mobile vehicles (MVs) for energy and data collection.
• Existing methods using multiple MVs face limitations in practical environments and data collection efficiency.
• Complex terrains and remote areas pose challenges for traditional vehicle-based data collection.

Purpose of the Study:

• To propose a novel framework integrating MVs and unmanned aerial vehicles (UAVs) for enhanced data collection in wireless rechargeable sensor networks.
• To prolong network lifetime and reduce data overflow by optimizing data collection and energy replenishment strategies.
• To address the limitations of MVs in complex environments and improve overall network performance.

Main Methods:

• Developed an optimal charging algorithm to determine the best charging sequence for MVs.
• Implemented a strategy for selecting neighboring clusters to optimize data offloading to MVs, reducing data overflow.
• Designed a UAV scheduling algorithm to assist MVs in collecting buffered data, further mitigating data overflow.

Main Results:

• The proposed charging algorithm effectively determines the optimal charging order for MVs.
• The cluster selection strategy successfully reduces data overflow during MV charging periods.
• The integrated MV and UAV framework significantly minimizes data loss and maximizes network lifetime.

Conclusions:

• The novel framework combining MVs and UAVs offers a robust solution for data collection in wireless rechargeable sensor networks.
• Optimized charging orders and data offloading strategies are crucial for extending network longevity.
• The synergistic use of MVs and UAVs effectively overcomes environmental limitations and enhances network efficiency.