Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Rechargeable Sensor Networks (WRSNs) are increasingly used in diverse Internet of Things (IoT) applications.
• Dynamic changes in sensor node energy consumption pose challenges for existing charging scheduling methods.
• Inaccurate energy requirement estimation can lead to critical node failure and event loss.

Purpose of the Study:

• To develop a charging scheduling method for WRSNs that addresses dynamic energy consumption and spatial imbalance.
• To minimize the Event Missing Rate (EMR) by accurately predicting node energy needs.
• To ensure network stability and reliability in scenarios with fluctuating energy demands.

Main Methods:

• Proposed an Energy Consumption Balanced Tree (ECBT) construction to extend node lifetime.
• Transformed the problem into Maximizing the Evaluation of each node's Energy Consumption Rate prediction (MEECR).
• Solved the MEECR problem, a variant of the knapsack problem, using dynamic programming and developed a charging scheme (DCNM) considering node energy needs and mobile charger capabilities.

Main Results:

• The proposed method effectively balances spatial and temporal energy consumption dynamics.
• Accurate prediction of node energy consumption rates was achieved.
• The developed charging scheduling scheme (DCNM) met dual constraints of node requirements and mobile charger capabilities.

Conclusions:

• The novel charging scheduling method significantly reduces the Event Missing Rate (EMR) in WRSNs with dynamic energy consumption.
• The approach outperforms existing methods, showing average EMR reductions of 35.2% and 26.9%.
• This work provides a robust solution for reliable WRSN operation in demanding IoT applications.