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2000
Volume 15, Issue 4
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

Introduction

The increasing expansions of IoT networks enforce the adoption of efficient resource allocation, energy management, and network congestion control.

Methods

In this regard, this paper proposes a brand-new hybrid quantum-driven optimization model integrating Pyramid Quantum Neural Network (Py-QNN), Deep Long Short-Term Memory (DLSTM), and Multi-fragmented Jaya Puzzle Optimization (FJPO). This optimizes energy consumption to the minimum, latency to the minimum, throughput to the maximum, and network lifetime by increasing the multi-layer architecture of cluster-based communication. A comparative study with models like LEACH, PEGASIS, and Direct Transmission shows better performance.

Results

Simulation results show a reduction in energy consumption by up to 60%, 30-50% lower communication delay, and a throughput increase of 25%.

Conclusion

The proposed model is scalable and adaptable in real-time. Hence, it is suitable for large-scale dynamic IoT environments.

© 2025 Bentham Science Publishers
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2025-07-28
2026-01-02

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A Hybrid Quantum-driven Optimization Model for Congestion-aware and Energy-efficient Resource Allocation in IoT Networks
Int J Sensors Wirel Commun Control 15, 391 (2025); https://doi.org/10.2174/0122103279371719250225043246
/content/journals/swcc/10.2174/0122103279371719250225043246
/content/journals/swcc/10.2174/0122103279371719250225043246
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  • Article Type:     Research Article
Keyword(s): delay; energy consumption; IoT; network duration; quantum neural networks; resource management; routing congestion control; system throughput

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