Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) are crucial for smart applications, but high sensor density generates redundant data, straining network resources.
• Existing data aggregation schemes often compromise data accuracy and increase end-to-end delay to reduce transmission overhead.

Purpose of the Study:

• To propose AggOR, an energy-efficient data aggregation scheme for WSNs that maintains high information accuracy.
• To address the challenge of redundant data in high-density WSNs.

Main Methods:

• Developed a data aggregation scheme, AggOR, based on the overlapping rate of sensing areas.
• Selected gathering nodes considering candidate parents, neighbors, and a preset overlapping rate threshold.
• Cleaned redundant data by leveraging the high correlation of data within a gathering area.

Main Results:

• AggOR effectively cleans redundant data by identifying and utilizing highly correlated data within gathering areas.
• The scheme preserves original data entropy by exclusively removing duplicate information.
• Experimental results demonstrate AggOR's superior data accuracy and reduced end-to-end delay compared to existing methods.

Conclusions:

• AggOR offers an energy-efficient solution for data collection in WSNs, balancing data accuracy and network resource management.
• The proposed scheme achieves high information fidelity and short end-to-end delays, comparable network lifetime.
• AggOR is a promising approach for optimizing data aggregation in high-density wireless sensor networks.