CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless sensor networks (WSNs) face challenges in energy efficiency and data aggregation.
• Existing protocols often struggle with balanced energy consumption and optimal routing.

Purpose of the Study:

• To propose an energy-efficient collaborative data aggregation protocol for WSNs.
• To integrate topology control, Multiple Access Control (MAC), and routing for optimized performance.
• To minimize overall network energy consumption and balance node energy.

Main Methods:

• Utilizing a bio-level Voronoi diagram for network organization and data gathering.
• Implementing optimal clustering for sensor nodes.
• Employing particle swarm optimization (PSO) for inter-cluster communication path selection.
• Minimizing maximum next hop distance and hop count for efficient routing.

Main Results:

• The proposed protocol demonstrates superior energy efficiency compared to traditional routing protocols.
• Synchronization accuracy is significantly improved.
• Achieved balanced energy consumption across all nodes in the network.
• Reduced overall network energy consumption through optimized multi-hop communication.

Conclusions:

• The bio-level Voronoi diagram-based collaborative data aggregation protocol offers substantial energy savings in WSNs.
• PSO-based path optimization effectively minimizes energy consumption and hop count.
• The protocol enhances network lifetime and reliability through balanced energy distribution.