Quantum-Driven Adaptive Dynamic Clustering and Compressive Sensing Framework for Scalable, Energy-Efficient Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) play a critical role in smart cities, healthcare, and industrial Internet of Things (IoT) applications, yet they face severe challenges due to limited energy, scalability constraints, and uneven load distribution. Existing clustering and compressive sensing approaches often optimize only one aspect, leading to premature energy depletion and reduced throughput. This paper presents a Quantum-Driven Adaptive Dynamic Clustering and Compressive Sensing (QDAC-CS) framework that integrates Quantum Particle Swarm Optimization (QPSO), hybrid compressive sensing, and fuzzy logic-based relay selection into a unified solution. The framework adaptively adjusts cluster sizes, balances routing load, and reduces data transmission costs. Simulation results demonstrate that QDAC-CS improves network lifetime by more than 25%, enhances throughput by 27%, and achieves a 45% reduction in data payload with less than 5% reconstruction error. These results confirm the scalability, robustness, and suitability of the proposed framework for next-generation IoT-enabled WSNs.

Introduction

Wireless Sensor Networks (WSNs) are key components of smart environments and industrial IoT, composed of low-power nodes that sense and transmit data to a base station. However, WSN performance is limited by energy constraints, high communication costs, and uneven traffic, which reduce network lifetime. Traditional clustering protocols like LEACH and LEACH-C either suffer from unbalanced energy use or lack scalability. Metaheuristic approaches (e.g., PSO, GWO, SCA) improve energy efficiency but often converge prematurely and handle clustering, routing, and compression separately.

To address these limitations, the paper proposes QDAC-CS (Quantum-Driven Adaptive Dynamic Clustering and Compressive Sensing) — an integrated framework that optimizes clustering, routing, and data compression within a single adaptive system.

Key Components & Contributions

1. Quantum-Enhanced Clustering (QPSO): Uses Sobol initialization, Lévy flights, and Gaussian perturbations for adaptive Cluster Head (CH) selection and energy-balanced scheduling.

2. Hybrid Compressive Sensing (CS–GWO): Employs Grey Wolf Optimization to design recovery matrices for low payload and accurate signal reconstruction.

3. Dynamic Cluster Resizing: Adjusts cluster sizes based on residual energy and node density.

4. Fuzzy Multi-Hop Routing: Uses fuzzy logic rules to select optimal relay nodes considering energy, distance, and deviation.

5. Performance Gains: Extends network lifetime by over 25% and increases throughput by more than 27% compared to existing protocols.

Methodology & Simulation

• Network setup: 100 nodes in a 100×100 m² area, 2 J initial energy, 2000 simulation rounds.

• Comparative protocols: LEACH, LEACH-C, SCA-Lévy, PSO-GWO.

• Performance metrics: First Node Death (FND), Half Node Death (HND), Last Node Death (LND), throughput, and energy consumption.

Results

• FND: 820 rounds (vs. 300 in LEACH, 600 in PSO-GWO).

• HND: 1320 rounds; LND: 1500 rounds.

• Throughput: ~35,400 packets (vs. 27,800 in PSO-GWO).

• Data compression: Reduces transmission load by ~45% with <5% reconstruction error.

• Energy usage: More balanced per round, significantly extending node lifespan.

Conclusion

This paper presented the Quantum-Driven Adaptive Dynamic Clustering and Compressive Sensing (QDAC-CS) framework, which unifies QPSO, hybrid CS-GWO, dynamic cluster resizing, and fuzzy multi-hop routing to improve scalability and energy efficiency in WSNs. Simulation results demonstrate significant improvements in network lifetime, throughput, and transmission efficiency compared with state-of-the-art methods. Future work will explore lightweight blockchain-based trust management and hardware-in-the-loop validation for real-world IoT and industrial deployments.

References

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Copyright

Copyright © 2025 Arun Joseph A, Maivizhi A, Vishnupriya S, Saranya K, Anitha U. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.