Multi-Factor Cost Function-Based Interference-Aware Clustering with Voronoi Cell Partitioning for Dense WSNs

Area of Science:

• Computer Science
• Network Engineering
• Wireless Communication

Background:

• Traditional clustering algorithms like LEACH and HEED struggle with dense wireless sensor networks (WSNs) due to unbalanced load distribution and high contention.
• Existing methods often result in overloaded cluster heads (CHs) in dense areas and underutilized CHs in sparse regions, leading to frequent CH changes and reduced network efficiency.
• This inefficiency is particularly problematic in dynamic, real-time environments requiring stable network operation.

Purpose of the Study:

• To develop a Density-Aware Adaptive Clustering (DAAC) protocol for optimizing CH selection and cluster formation in dense WSNs.
• To address the limitations of traditional algorithms by incorporating node density and link quality into CH selection metrics.
• To enhance overall network lifetime, packet delivery ratio, and throughput in dense WSN deployments.

Main Methods:

• Developed the DAAC protocol, utilizing residual energy, local node density, and link quality as a unified CH detection metric.
• Implemented a minimum inter-CH distance constraint to prevent CH crowding and used a multi-factor cost function for cluster formation.
• Incorporated dynamic re-clustering triggered by CH energy depletion or significant load density changes, and employed dynamic Voronoi cells (VCs) for interference-aware coverage.

Main Results:

• DAAC demonstrated a network lifetime improvement of 20.53% over LEACH and 32.51% over HEED.
• The protocol achieved an average increase in packet delivery ratio of 8.14% (vs. LEACH) and 25.68% (vs. HEED).
• Total throughput packet saw significant enhancements: 140.15% over LEACH and 883.51% over HEED.

Conclusions:

• DAAC effectively optimizes CH selection and cluster formation in dense WSNs, outperforming traditional algorithms like LEACH and HEED.
• The protocol's adaptive nature and use of Voronoi cells contribute to improved network lifetime, data reliability, and overall performance.
• DAAC offers a robust solution for dense WSNs, enabling efficient hierarchical extensions and secondary CHs in extremely dense scenarios.