An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networks
The Internet of Things (IoT) plays a pivotal role in modern society, connecting everyday objects to the internet and enabling smart functionalities that enhance efficiency, convenience, and sustainability across multiple sectors. As IoT networks have expanded in scale and complexity, optimising ener...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | International Journal of Cognitive Computing in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307425000105 |
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| author | Yousra Mahmoudi Nadjet Zioui Hacène Belbachir |
| author_facet | Yousra Mahmoudi Nadjet Zioui Hacène Belbachir |
| author_sort | Yousra Mahmoudi |
| collection | DOAJ |
| description | The Internet of Things (IoT) plays a pivotal role in modern society, connecting everyday objects to the internet and enabling smart functionalities that enhance efficiency, convenience, and sustainability across multiple sectors. As IoT networks have expanded in scale and complexity, optimising energy consumption has become critical to ensuring their long-term sustainability and cost-effectiveness. This paper introduces quantum-inspired particle swarm optimisation clustering (QIPSOC), an innovative metaheuristic that relies on a novel mathematical formulation (MPMC) and integrates principles from quantum computing and particle swarm optimisation to achieve energy-efficient clustering, balancing network performance constraints and energy consumption. By encoding particles as n-qubit systems and employing quantum-inspired motion through qubit rotations along with an inertial damping factor, QIPSOC enhances solution diversity, mitigates premature convergence to local optima, and guides the exploration of the solution space to achieve significant energy savings, ranging from 17.99 % to over 91 %, depending on the network configuration. These results underscore QIPSOC's superiority over state-of-the-art methods in optimising energy efficiency and performance in IoT networks, making it well-suited for real-world deployment scenarios requiring rapid decision-making. |
| format | Article |
| id | doaj-art-1bc7c40193b644f1a0a76449b4eb8250 |
| institution | Kabale University |
| issn | 2666-3074 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj-art-1bc7c40193b644f1a0a76449b4eb82502025-02-06T05:12:51ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742025-12-016313322An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networksYousra Mahmoudi0Nadjet Zioui1Hacène Belbachir2Université du Québec à Trois-Rivières, 3351 Boulevard des Forges, Trois-Rivières, QC G8Z 4M3, Canada; Laboratoire RECITS, Université de la Science et de la Technologie Houari Boumediene, BP 32 Bab Ezzouar, 16111 Algiers, Algeria; Corresponding author.Université du Québec à Trois-Rivières, 3351 Boulevard des Forges, Trois-Rivières, QC G8Z 4M3, CanadaLaboratoire RECITS, Université de la Science et de la Technologie Houari Boumediene, BP 32 Bab Ezzouar, 16111 Algiers, AlgeriaThe Internet of Things (IoT) plays a pivotal role in modern society, connecting everyday objects to the internet and enabling smart functionalities that enhance efficiency, convenience, and sustainability across multiple sectors. As IoT networks have expanded in scale and complexity, optimising energy consumption has become critical to ensuring their long-term sustainability and cost-effectiveness. This paper introduces quantum-inspired particle swarm optimisation clustering (QIPSOC), an innovative metaheuristic that relies on a novel mathematical formulation (MPMC) and integrates principles from quantum computing and particle swarm optimisation to achieve energy-efficient clustering, balancing network performance constraints and energy consumption. By encoding particles as n-qubit systems and employing quantum-inspired motion through qubit rotations along with an inertial damping factor, QIPSOC enhances solution diversity, mitigates premature convergence to local optima, and guides the exploration of the solution space to achieve significant energy savings, ranging from 17.99 % to over 91 %, depending on the network configuration. These results underscore QIPSOC's superiority over state-of-the-art methods in optimising energy efficiency and performance in IoT networks, making it well-suited for real-world deployment scenarios requiring rapid decision-making.http://www.sciencedirect.com/science/article/pii/S2666307425000105Quantum computingMathematical modellingParticle swarm optimisationEnergy consumptionInternet of Things |
| spellingShingle | Yousra Mahmoudi Nadjet Zioui Hacène Belbachir An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networks International Journal of Cognitive Computing in Engineering Quantum computing Mathematical modelling Particle swarm optimisation Energy consumption Internet of Things |
| title | An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networks |
| title_full | An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networks |
| title_fullStr | An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networks |
| title_full_unstemmed | An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networks |
| title_short | An improved quantum-inspired particle swarm optimisation approach to reduce energy consumption in IoT networks |
| title_sort | improved quantum inspired particle swarm optimisation approach to reduce energy consumption in iot networks |
| topic | Quantum computing Mathematical modelling Particle swarm optimisation Energy consumption Internet of Things |
| url | http://www.sciencedirect.com/science/article/pii/S2666307425000105 |
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