A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Wireless Sensor Networks (WSNs) face challenges in network lifetime and energy efficiency.
• Existing clustering algorithms often struggle to optimize energy consumption and data transmission effectively.

Purpose of the Study:

• To propose an improved clustering algorithm for WSNs to enhance network lifetime and efficiency.
• To optimize cluster head selection and energy-efficient routing strategies within WSNs.

Main Methods:

• Developed an enhanced fuzzy spider monkey optimization technique for cluster head selection.
• Integrated a hidden Markov model-based clustering algorithm considering energy, density, and position.
• Enhanced energy-efficient routing strategies and introduced a polling control method for steady transmission periods.

Main Results:

• The proposed model demonstrates a 1.2% improvement in overall network performance.
• The algorithm effectively balances energy consumption and network efficiency.
• Optimized cluster head selection and routing contribute to extended network lifetime.

Conclusions:

• The enhanced clustering algorithm offers a significant improvement in WSN performance and longevity.
• The integration of advanced optimization and modeling techniques addresses key WSN challenges.
• This approach provides a viable solution for more efficient and sustainable WSN operations.