Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks.

Area of Science:

• Computer Science
• Electrical Engineering
• Networking

Background:

• Wireless Sensor Networks (WSNs) are crucial for the Internet of Things (IoTs).
• Energy efficiency is a major challenge for resource-constrained sensor nodes (SNs) in WSNs.
• Effective clustering and cluster head (CH) selection are vital for extending WSN lifespan.

Purpose of the Study:

• To propose an energy-efficient clustering and CH selection technique for next-generation WSNs (NG-WSNs).
• To enhance the network lifetime and energy efficiency of WSNs.

Main Methods:

• A midpoint clustering technique considering residual energy and node distance.
• A four-layer hierarchical network including SNs, CHs, UAVs, and a Base Station (BS).
• Multihop communication for distant CHs and simulated annealing for optimal UAV trajectory.

Main Results:

• The proposed approach achieves balanced clusters and uniform sensor distribution.
• UAVs provide mobility, shortening communication ranges and extending network lifetime.
• Experimental results demonstrate superior energy efficiency and network longevity over state-of-the-art techniques.

Conclusions:

• The developed clustering and CH selection technique significantly improves WSN performance.
• The integration of UAVs and optimized trajectory planning enhances energy efficiency.
• This method offers a promising solution for energy-constrained NG-WSNs.