An Energy Efficient Load Balancing Tree-Based Data Aggregation Scheme for Grid-Based Wireless Sensor Networks.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) are large-scale networks of low-cost, easily deployable sensors.
• Efficient data aggregation and energy management are critical challenges in WSNs.
• Existing methods often suffer from excessive energy consumption and hotspot issues.

Purpose of the Study:

• To propose an energy-efficient load balancing tree-based data aggregation scheme (LB-TBDAS) for grid-based WSNs.
• To enhance the network lifetime and reduce overall energy consumption in WSNs.
• To mitigate the hotspot problem in WSNs.

Main Methods:

• Partitioning the sensing area into a grid and electing a cell head with maximum residual energy in each cell.
• Establishing a tree-like data transmission path using the minimum spanning tree algorithm with constraints on energy consumption, network depth, and child nodes.
• Implementing a data aggregation process where cell heads collect and transmit data to the base station (BS) along the established tree path.

Main Results:

• LB-TBDAS demonstrates significantly lower total energy consumption compared to GB-PEDAP and PEDAP.
• The proposed LB-TBDAS extends the network lifetime by more than 100% compared to existing schemes.
• LB-TBDAS effectively avoids excessive energy consumption during multi-hop data transmission and prevents the hotspot problem.

Conclusions:

• The LB-TBDAS is a highly effective scheme for energy-efficient data aggregation in grid-based WSNs.
• The proposed scheme offers substantial improvements in network lifetime and energy conservation.
• LB-TBDAS provides a robust solution for addressing critical challenges in WSN deployment and operation.