Energy aware swarm optimization with intercluster search for wireless sensor network

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) offer low-cost solutions for diverse applications.
• WSNs face power constraints due to deployment challenges and difficulty in battery replacement.
• Efficient communication with minimal power consumption is a critical challenge in WSNs.

Purpose of the Study:

• To propose an improved binary particle swarm optimization (PSO) algorithm for WSNs.
• To optimize the number of clusters and cluster heads (CHs) for enhanced network performance.
• To address the challenge of negligible power utilization in WSN communication.

Main Methods:

• An improved binary particle swarm optimization (PSO) algorithm was developed.
• A modified connected dominating set (CDS) approach based on residual energy was integrated.
• Simulations were conducted to evaluate the performance of the proposed algorithms.

Main Results:

• The proposed BPSO-T and BPSO-EADS algorithms demonstrate superior performance compared to LEACH and standard PSO.
• Significant improvements in energy savings were observed.
• Enhanced Quality of Service (QoS) was achieved in the simulated WSNs.

Conclusions:

• The developed PSO-based approach effectively optimizes WSN cluster formation.
• The proposed methods provide a viable solution for improving energy efficiency and QoS in power-constrained WSNs.
• This research contributes to the advancement of sustainable and efficient wireless sensor network design.