Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Energy conservation is a critical challenge in Wireless Sensor Networks (WSNs).
• Existing single cluster head (CH) models often face limitations in energy efficiency and data acquisition.
• Mobile sinks (MS) introduce complexities in optimizing data collection routes.

Purpose of the Study:

• To propose and evaluate a dual cluster head (CH) optimization strategy for WSNs.
• To reduce energy consumption and minimize data acquisition delay in WSNs.
• To enhance the overall network lifetime and data reception rate.

Main Methods:

• Fuzzy c-means clustering and multi-objective particle swarm optimization for dual CH selection.
• Improved ant colony algorithm for optimizing the mobile sink (MS) trajectory.
• Comparative analysis of network lifetime, node death rounds, and packet reception rates.

Main Results:

• The proposed dual CH approach significantly increased the number of dead rounds for the first node and 50% of nodes compared to existing methods.
• Base station packet reception rates showed substantial improvements, indicating enhanced data transmission efficiency.
• The optimized MS trajectory reduced the overall data collection path length by approximately 10%.

Conclusions:

• Dual CH optimization effectively conserves energy and extends the operational lifetime of WSNs.
• The integrated approach of optimized CH selection and MS trajectory planning offers a superior solution for WSN performance.
• This strategy presents a promising direction for developing more efficient and sustainable wireless sensor networks.