Implementation and Performance Evaluation of Quantum-Inspired Clustering Scheme for Energy-Efficient WSNs

Area of Science:

• Computer Science
• Electrical Engineering
• Quantum Computing

Background:

• Wireless Sensor Networks (WSNs) are crucial for IoT, but energy consumption is a major limitation due to battery constraints.
• Effective cluster head selection is vital for reducing energy usage in WSNs.
• Quantum computing offers novel algorithmic approaches for complex optimization problems.

Purpose of the Study:

• To introduce a novel, energy-efficient clustering scheme for WSNs using quantum-inspired algorithms.
• To enhance cluster head selection for improved network performance and longevity.
• To address the energy consumption challenges in WSNs for scalable IoT applications.

Main Methods:

• Development of a Quantum-Inspired Clustering Scheme (QICS) based on the Quantum Grover algorithm.
• Simulation of QICS against established algorithms (LEACH, GSACP, EDS-KHO) using 100 sensor nodes.
• Comparative analysis of network lifetime, energy consumption, and packet delivery ratio.

Main Results:

• QICS extended network lifetime by 30.5% compared to existing methods.
• Energy usage was reduced by 22.4% with the proposed QICS.
• Packet delivery ratios improved by 19.8%, demonstrating enhanced network efficiency.

Conclusions:

• Quantum-inspired techniques offer a promising solution for WSN energy restrictions.
• QICS significantly outperforms traditional clustering algorithms in WSN performance metrics.
• This approach highlights the potential of quantum-inspired methods for energy-aware and scalable IoT systems.