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Updated: Jan 15, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
An energy aware cluster inspired routing protocol using multi strategy improved crayfish optimization algorithm for
R Chithra1, C K Sarumathiy2, E Padma3
1Department of Information Technology, K. S. Rangasamy College of Technology, Tiruchengode, Namakkal, Tamil Nadu, 637 215, India. chithra@ksrct.ac.in.
This study introduces a novel intelligent clustering mechanism for the Internet of Things (IoT) to enhance energy efficiency and network longevity. The proposed method optimizes cluster head selection, significantly improving throughput and reducing transmission delays in IoT networks.
Area of Science:
- Computer Science
- Electrical Engineering
- Environmental Science
Background:
- The Internet of Things (IoT) connects billions of devices, leading to significant environmental and economic impacts.
- Energy consumption is critical for IoT sensor operations, but rapid energy drain threatens performance and stability.
- Clustering with metaheuristic techniques offers a promising approach for energy management and quality of service in IoT.
Purpose of the Study:
- To propose an intelligent clustering mechanism for green communication in IoT, maximizing network lifetime.
- To address the NP-hard problem of energy management in large-scale IoT deployments.
- To enhance the performance of IoT networks through optimized energy utilization and cluster head selection.
Main Methods:
- Developed a multi-strategy-improved crayfish optimization algorithm-based intelligent clustering mechanism (MSCFOAICM).
- Implemented a multi-objective fitness function considering delay, energy, distance, jitter, and packet forwarding potential.
- Utilized a hybrid BWM-TOPSIS multicriteria decision-making model for trust assessment and selection of energy-potent, non-malicious cluster heads.
Main Results:
- The MSCFOAICM scheme achieved optimal cluster formation, sustaining maximized energy and extending network lifetime.
- Demonstrated a throughput improvement of 18.14% and an increase in operating IoT nodes by 19.42%.
- Achieved a reduction in mean transmission delay by 18.42% compared to baseline schemes.
Conclusions:
- The proposed MSCFOAICM effectively enhances green communication in IoT by optimizing energy efficiency and network lifespan.
- The intelligent clustering mechanism provides a robust solution for managing energy stability and performance in large-scale IoT networks.
- MSCFOAICM significantly outperforms existing methods in terms of throughput, operational nodes, and transmission delay.
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