Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Sensor networks are crucial for data collection, but existing algorithms assume uniform initial sensor energy, which is unrealistic.
• Varying initial energy levels in practical sensor networks significantly impact data aggregation efficiency and network lifespan.

Purpose of the Study:

• To propose a novel algorithm, Data Aggregation with Different Initial Energy (DADIE), for sensor networks with heterogeneous initial energy levels.
• To enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in such networks.

Main Methods:

• DADIE considers transmission distance and initial energy levels for network topology formation, limiting child nodes.
• The aggregation tree is reconstructed each round, prioritizing nodes with higher initial energy and proximity to the receiver.
• Secure transmission channels and slice-and-mix technology are employed to enhance network security.

Main Results:

• DADIE effectively reduces communication overhead and energy consumption by 5-20%.
• Security is improved by 10-20%, and node mortality is reduced by 10-30% compared to existing algorithms.
• Experimental simulations confirm DADIE's effectiveness in resolving data aggregation challenges in heterogeneous sensor networks.

Conclusions:

• DADIE offers a practical solution for data aggregation in sensor networks with varying initial energy levels.
• The algorithm significantly improves network efficiency, security, and longevity.
• DADIE demonstrates superior performance over existing methods in key performance indicators.